knitr::opts_chunk$set(warning = FALSE)
source("Raman_functions.R")
library(png)
library(bmp)
library(pixmap)
library(baseline)
##
## Attaching package: 'baseline'
## The following object is masked from 'package:stats':
##
## getCall
library(hyperSpec)
## Loading required package: lattice
## Loading required package: grid
## Loading required package: ggplot2
## Loading required package: xml2
## Package hyperSpec, version 0.100.0
##
## To get started, try
## vignette ("hyperspec")
## package?hyperSpec
## vignette (package = "hyperSpec")
##
## If you use this package please cite it appropriately.
## citation("hyperSpec")
## will give you the correct reference.
##
## The project homepage is http://hyperspec.r-forge.r-project.org
library(prospectr)
## [34mprospectr version 0.2.6 -- [39m'chicago'
## [34mcheck the github repository at: https://github.com/l-ramirez-lopez/prospectr/[39m
##
## Attaching package: 'prospectr'
## The following object is masked from 'package:baseline':
##
## baseline
library(ggplot2)
library(magrittr)
## Warning: package 'magrittr' was built under R version 4.0.5
library(ggpubr)
##
## Attaching package: 'ggpubr'
## The following object is masked from 'package:hyperSpec':
##
## mean_sd
library(signal)
##
## Attaching package: 'signal'
## The following object is masked from 'package:prospectr':
##
## resample
## The following objects are masked from 'package:stats':
##
## filter, poly
library(OpenSpecy)
## This version of bslib is designed to work with shiny version 1.6.0 or higher.
library(tidyverse)
## ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.0 ──
## ✔ tibble 3.1.2 ✔ dplyr 1.0.6
## ✔ tidyr 1.1.3 ✔ stringr 1.4.0
## ✔ readr 1.4.0 ✔ forcats 0.5.0
## ✔ purrr 0.3.4
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::collapse() masks hyperSpec::collapse()
## ✖ tidyr::extract() masks magrittr::extract()
## ✖ dplyr::filter() masks signal::filter(), stats::filter()
## ✖ dplyr::lag() masks stats::lag()
## ✖ purrr::set_names() masks magrittr::set_names()
The goal of this project is to not only enumerate total plastics but to also identify the and enumerate the plastic polymers polluting the Southern Ocean. This project’s goal is to identify the
read in .txt files
test2_10_8 <- read.delim(file = "ren4EB4_0__Time_0_test2_10_8.txt", header = F, sep = "\t")
#format table
colnames(test2_10_8) <- c("Raman_shift", "Counts")
test2_10_8 <- as.data.frame(test2_10_8)
#repeat for each table
test_control <- read.delim("renB232_0__Time_0_test_control.txt", header = F, sep = "\t")
colnames(test_control) <- c("Raman_shift", "Counts")
test_control <- as.data.frame(test_control)
pot_plastic <- read.delim("ren79C7_0__Time_0_potentialPlastic_100ul.txt", header = F, sep = "\t")
colnames(pot_plastic) <- c("Raman_shift", "Counts")
pot_plastic <- as.data.frame(pot_plastic)
pot_plastic1 <- read.delim("ren76AF_0__Time_0_potPla1_100ul.txt", header = F, sep = "\t")
colnames(pot_plastic1) <- c("Raman_shift", "Counts")
pot_plastic1 <- as.data.frame(pot_plastic1)
b <- list(test_control, pot_plastic,pot_plastic1, test2_10_8)
head(b)
## [[1]]
## Raman_shift Counts
## 1 1930.3320 13335.80664
## 2 1928.7832 13318.06445
## 3 1927.2324 13339.31250
## 4 1925.6816 13378.28613
## 5 1924.1309 13141.61231
## 6 1922.5801 13425.56445
## 7 1921.0273 13259.87598
## 8 1919.4746 13297.22461
## 9 1917.9238 13233.09766
## 10 1916.3711 13278.48633
## 11 1914.8164 13207.95410
## 12 1913.2637 13420.70508
## 13 1911.7109 13192.48828
## 14 1910.1562 13149.37012
## 15 1908.6016 13376.47070
## 16 1907.0469 13170.92676
## 17 1905.4922 13195.36328
## 18 1903.9355 13280.86523
## 19 1902.3809 13086.72754
## 20 1900.8242 13271.82422
## 21 1899.2676 13195.02734
## 22 1897.7109 13142.35254
## 23 1896.1543 13141.07715
## 24 1894.5977 13226.48144
## 25 1893.0391 13059.89453
## 26 1891.4805 13178.97363
## 27 1889.9219 13241.85742
## 28 1888.3633 13485.93750
## 29 1886.8047 13203.99609
## 30 1885.2441 13170.65137
## 31 1883.6855 13140.52637
## 32 1882.1250 13342.75586
## 33 1880.5645 13046.67578
## 34 1879.0039 13144.73047
## 35 1877.4414 13050.58691
## 36 1875.8809 13203.03125
## 37 1874.3184 13020.88379
## 38 1872.7559 13058.04981
## 39 1871.1934 13168.79590
## 40 1869.6309 13087.54785
## 41 1868.0664 13260.60938
## 42 1866.5039 13214.55176
## 43 1864.9395 13155.72656
## 44 1863.3750 13151.26465
## 45 1861.8105 13226.69531
## 46 1860.2461 12945.85938
## 47 1858.6797 13083.58984
## 48 1857.1152 13195.71289
## 49 1855.5488 13149.74121
## 50 1853.9824 12918.64453
## 51 1852.4160 13201.47461
## 52 1850.8496 13122.02930
## 53 1849.2812 13127.15527
## 54 1847.7129 13097.19629
## 55 1846.1465 13177.26074
## 56 1844.5781 13019.78418
## 57 1843.0078 12956.40137
## 58 1841.4395 13089.02344
## 59 1839.8711 12995.38769
## 60 1838.3008 13064.24023
## 61 1836.7305 12989.75781
## 62 1835.1602 13150.91309
## 63 1833.5898 12998.45703
## 64 1832.0176 13035.42383
## 65 1830.4473 12938.74023
## 66 1828.8750 12875.50000
## 67 1827.3027 12902.92969
## 68 1825.7305 12933.52637
## 69 1824.1582 12933.90918
## 70 1822.5840 12827.81738
## 71 1821.0117 12817.10156
## 72 1819.4375 12717.43555
## 73 1817.8633 12863.97461
## 74 1816.2891 12915.18164
## 75 1814.7129 12869.53320
## 76 1813.1387 12850.88379
## 77 1811.5625 12919.51660
## 78 1809.9863 12910.38379
## 79 1808.4102 12701.40723
## 80 1806.8340 12755.75586
## 81 1805.2578 12765.68945
## 82 1803.6797 12794.63965
## 83 1802.1016 12923.41406
## 84 1800.5254 12741.59473
## 85 1798.9473 12654.89648
## 86 1797.3672 12744.02637
## 87 1795.7891 12662.11426
## 88 1794.2090 12774.94922
## 89 1792.6309 12740.54688
## 90 1791.0508 12918.19141
## 91 1789.4707 12746.13965
## 92 1787.8887 12819.31738
## 93 1786.3086 12691.63769
## 94 1784.7266 12718.94727
## 95 1783.1445 12953.30859
## 96 1781.5625 12771.95215
## 97 1779.9805 12939.84277
## 98 1778.3984 12875.45801
## 99 1776.8145 12970.60742
## 100 1775.2324 13008.87500
## 101 1773.6484 12922.41602
## 102 1772.0645 13045.90332
## 103 1770.4805 13191.42773
## 104 1768.8945 13089.20898
## 105 1767.3105 13397.11816
## 106 1765.7246 13444.70606
## 107 1764.1387 13307.81348
## 108 1762.5527 13506.71777
## 109 1760.9668 13711.83984
## 110 1759.3789 13883.78711
## 111 1757.7930 13967.44824
## 112 1756.2051 14128.24219
## 113 1754.6172 14175.60059
## 114 1753.0293 14229.23438
## 115 1751.4414 14536.23926
## 116 1749.8516 14868.28711
## 117 1748.2637 14485.96387
## 118 1746.6738 14732.94727
## 119 1745.0840 14723.50293
## 120 1743.4941 14993.91113
## 121 1741.9023 14956.12207
## 122 1740.3125 14941.92090
## 123 1738.7207 14962.29199
## 124 1737.1289 14666.91504
## 125 1735.5371 14725.02734
## 126 1733.9453 14509.90625
## 127 1732.3535 14396.91504
## 128 1730.7598 14153.70410
## 129 1729.1680 14122.43359
## 130 1727.5742 14008.02832
## 131 1725.9805 13664.67285
## 132 1724.3848 13625.68457
## 133 1722.7910 13549.08594
## 134 1721.1953 13483.49219
## 135 1719.6016 13383.44824
## 136 1718.0059 13444.82910
## 137 1716.4082 13087.93359
## 138 1714.8125 13191.63574
## 139 1713.2168 13104.26562
## 140 1711.6191 13098.33691
## 141 1710.0215 13045.45606
## 142 1708.4238 13089.61523
## 143 1706.8262 12958.53418
## 144 1705.2285 13170.05566
## 145 1703.6289 13084.36328
## 146 1702.0312 12972.12891
## 147 1700.4316 13060.02246
## 148 1698.8320 13241.64746
## 149 1697.2305 13429.44434
## 150 1695.6309 13421.89356
## 151 1694.0293 13617.37988
## 152 1692.4297 13826.83594
## 153 1690.8281 13972.19531
## 154 1689.2266 14364.09961
## 155 1687.6230 14613.83398
## 156 1686.0215 14902.46484
## 157 1684.4180 15399.98633
## 158 1682.8164 15699.24121
## 159 1681.2129 16074.76660
## 160 1679.6074 16534.30664
## 161 1678.0039 16667.92969
## 162 1676.4004 17175.44922
## 163 1674.7949 17542.54883
## 164 1673.1895 17579.30469
## 165 1671.5840 18008.44727
## 166 1669.9785 18186.75781
## 167 1668.3730 18461.49414
## 168 1666.7656 18578.94141
## 169 1665.1582 18688.55859
## 170 1663.5508 18739.01953
## 171 1661.9434 18693.03906
## 172 1660.3359 19031.12109
## 173 1658.7285 18986.65039
## 174 1657.1191 19105.38477
## 175 1655.5098 18826.28711
## 176 1653.9004 18979.18164
## 177 1652.2910 18886.60938
## 178 1650.6816 18834.44922
## 179 1649.0723 18668.97852
## 180 1647.4609 18565.66211
## 181 1645.8496 18408.07617
## 182 1644.2383 18210.21680
## 183 1642.6270 18198.58398
## 184 1641.0156 17974.48633
## 185 1639.4023 17514.80273
## 186 1637.7891 17123.52344
## 187 1636.1758 16932.34375
## 188 1634.5625 16578.54297
## 189 1632.9492 15913.50000
## 190 1631.3359 15518.24512
## 191 1629.7207 15149.48340
## 192 1628.1055 14805.64941
## 193 1626.4922 14565.67090
## 194 1624.8750 14172.58398
## 195 1623.2598 13920.46191
## 196 1621.6445 13639.05664
## 197 1620.0273 13401.07324
## 198 1618.4102 13229.67969
## 199 1616.7930 12964.05762
## 200 1615.1758 13126.67676
## 201 1613.5586 12779.28906
## 202 1611.9395 12643.70508
## 203 1610.3223 12540.61426
## 204 1608.7031 12536.40039
## 205 1607.0840 12681.95019
## 206 1605.4629 12626.76856
## 207 1603.8438 12577.78125
## 208 1602.2227 12516.47168
## 209 1600.6035 12587.85352
## 210 1598.9824 12443.28223
## 211 1597.3613 12408.25098
## 212 1595.7383 12362.44336
## 213 1594.1172 12282.74609
## 214 1592.4941 12221.57324
## 215 1590.8711 12166.58887
## 216 1589.2480 12108.54590
## 217 1587.6250 12173.72852
## 218 1586.0020 12269.67578
## 219 1584.3770 12227.04004
## 220 1582.7539 11947.41211
## 221 1581.1289 12020.26465
## 222 1579.5039 11919.24902
## 223 1577.8789 11847.49902
## 224 1576.2520 11997.20898
## 225 1574.6270 12048.46191
## 226 1573.0000 11784.59668
## 227 1571.3730 11878.90039
## 228 1569.7461 11911.70703
## 229 1568.1172 11899.95410
## 230 1566.4902 11759.20117
## 231 1564.8613 11873.38867
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## 234 1559.9746 11775.26856
## 235 1558.3457 11932.30762
## 236 1556.7148 12135.29102
## 237 1555.0859 12049.89844
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## 254 1527.3164 11989.48633
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## 261 1515.8535 12504.02832
## 262 1514.2148 12560.84570
## 263 1512.5762 12664.91211
## 264 1510.9355 12898.52734
## 265 1509.2949 12763.15723
## 266 1507.6543 12787.86328
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## 269 1502.7324 12679.14941
## 270 1501.0898 12898.75391
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## 272 1497.8047 12946.51269
## 273 1496.1621 13010.70117
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## 803 572.2148 11056.63965
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## 820 540.6797 9262.58106
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## 825 531.3789 8723.69922
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## 881 426.4785 9681.41602
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## 884 420.8203 9728.96191
## 885 418.9336 9720.06543
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## 890 409.4902 9764.30176
## 891 407.6016 9851.98340
## 892 405.7109 9856.29785
## 893 403.8203 9797.13867
## 894 401.9297 9882.10644
## 895 400.0391 9963.07031
## 896 398.1465 9807.46387
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## 898 394.3613 9547.97949
## 899 392.4668 9594.61231
## 900 390.5742 9349.46484
## 901 388.6797 9336.72754
## 902 386.7852 9276.49219
## 903 384.8906 9203.09180
## 904 382.9941 9075.64941
## 905 381.0996 8981.23242
## 906 379.2031 8827.53320
## 907 377.3066 8747.71582
## 908 375.4082 8874.81641
## 909 373.5117 8733.10644
## 910 371.6133 8864.10254
## 911 369.7148 8700.07519
## 912 367.8145 8733.59766
## 913 365.9160 8706.55957
## 914 364.0156 8724.27441
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## 918 356.4121 8756.92383
## 919 354.5098 8719.40332
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## 921 350.7051 8761.33691
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## 931 331.6504 8827.55469
## 932 329.7422 8994.55176
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## 936 322.1074 9056.92676
## 937 320.1973 9150.38086
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## 969 258.8438 12282.03027
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## 972 253.0684 13208.43066
## 973 251.1426 13304.06250
## 974 249.2148 13801.96582
## 975 247.2891 14150.42969
## 976 245.3613 14749.09277
## 977 243.4336 15145.16699
## 978 241.5059 15469.74805
## 979 239.5781 15970.47852
## 980 237.6484 16267.50781
## 981 235.7188 16753.57617
## 982 233.7891 16791.20703
## 983 231.8594 16774.42773
## 984 229.9277 16977.77734
## 985 227.9961 16984.26562
## 986 226.0645 17007.57617
## 987 224.1328 16685.39453
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## 989 220.2676 17148.58398
## 990 218.3340 17342.52148
## 991 216.4004 17452.34375
## 992 214.4668 17646.12500
## 993 212.5312 17844.99023
## 994 210.5957 18079.93750
## 995 208.6602 18242.44922
## 996 206.7246 18336.44922
## 997 204.7871 18214.80859
## 998 202.8516 18227.45898
## 999 200.9141 18077.51172
## 1000 198.9766 17839.90234
## 1001 197.0371 17701.72070
## 1002 195.0996 17278.61328
## 1003 193.1602 17045.21680
## 1004 191.2207 16488.39844
## 1005 189.2812 16359.67090
## 1006 187.3398 16082.83789
## 1007 185.3984 15910.46582
## 1008 183.4570 15726.58301
## 1009 181.5156 15703.72168
## 1010 179.5742 15710.47754
## 1011 177.6309 15854.93262
## 1012 175.6875 15901.53906
## 1013 173.7441 16081.90527
## 1014 171.8008 16318.77734
## 1015 169.8574 16655.83398
## 1016 167.9121 17055.72656
## 1017 165.9668 17614.80469
## 1018 164.0215 18051.56836
## 1019 162.0742 18680.82617
## 1020 160.1289 19167.24414
## 1021 158.1816 19962.89648
## 1022 156.2344 20786.43750
## 1023 154.2852 21504.36328
## 1024 152.3379 514.55194
## 1025 150.3887 30.78923
## 1026 148.4395 14.10857
## 1027 146.4902 29.49314
## 1028 144.5391 11.53821
## 1029 142.5898 19.22605
## 1030 140.6387 17.94031
## 1031 138.6875 -15.37397
##
## [[2]]
## Raman_shift Counts
## 1 1930.3320 2.730663e+05
## 2 1928.7832 2.736308e+05
## 3 1927.2324 2.737284e+05
## 4 1925.6816 2.738018e+05
## 5 1924.1309 2.729725e+05
## 6 1922.5801 2.725288e+05
## 7 1921.0273 2.724107e+05
## 8 1919.4746 2.727904e+05
## 9 1917.9238 2.723680e+05
## 10 1916.3711 2.716945e+05
## 11 1914.8164 2.713900e+05
## 12 1913.2637 2.717826e+05
## 13 1911.7109 2.718708e+05
## 14 1910.1562 2.710356e+05
## 15 1908.6016 2.709133e+05
## 16 1907.0469 2.721643e+05
## 17 1905.4922 2.713184e+05
## 18 1903.9355 2.708538e+05
## 19 1902.3809 2.701371e+05
## 20 1900.8242 2.700216e+05
## 21 1899.2676 2.704234e+05
## 22 1897.7109 2.700284e+05
## 23 1896.1543 2.693640e+05
## 24 1894.5977 2.694302e+05
## 25 1893.0391 2.693936e+05
## 26 1891.4805 2.694903e+05
## 27 1889.9219 2.685571e+05
## 28 1888.3633 2.684614e+05
## 29 1886.8047 2.681285e+05
## 30 1885.2441 2.679416e+05
## 31 1883.6855 2.678863e+05
## 32 1882.1250 2.683693e+05
## 33 1880.5645 2.675418e+05
## 34 1879.0039 2.671854e+05
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## 1015 169.8574 6.240473e+04
## 1016 167.9121 6.213617e+04
## 1017 165.9668 6.206822e+04
## 1018 164.0215 6.212366e+04
## 1019 162.0742 6.211227e+04
## 1020 160.1289 6.165010e+04
## 1021 158.1816 6.180576e+04
## 1022 156.2344 6.148251e+04
## 1023 154.2852 6.117225e+04
## 1024 152.3379 1.434586e+03
## 1025 150.3887 6.157846e+01
## 1026 148.4395 4.232571e+01
## 1027 146.4902 2.436390e+01
## 1028 144.5391 4.230678e+01
## 1029 142.5898 4.357906e+01
## 1030 140.6387 2.947336e+01
## 1031 138.6875 2.562329e+00
##
## [[3]]
## Raman_shift Counts
## 1 1930.3320 222753.78125
## 2 1928.7832 223002.17188
## 3 1927.2324 223275.45312
## 4 1925.6816 224243.48438
## 5 1924.1309 223036.67188
## 6 1922.5801 223419.37500
## 7 1921.0273 222583.85938
## 8 1919.4746 222280.29688
## 9 1917.9238 222933.48438
## 10 1916.3711 221828.15625
## 11 1914.8164 222205.90625
## 12 1913.2637 221514.85938
## 13 1911.7109 222188.59375
## 14 1910.1562 221388.48438
## 15 1908.6016 221394.50000
## 16 1907.0469 221784.82812
## 17 1905.4922 220859.96875
## 18 1903.9355 221817.60938
## 19 1902.3809 220955.82812
## 20 1900.8242 221074.43750
## 21 1899.2676 221164.07812
## 22 1897.7109 220807.26562
## 23 1896.1543 219519.40625
## 24 1894.5977 219970.51562
## 25 1893.0391 220427.84375
## 26 1891.4805 220410.07812
## 27 1889.9219 219877.39062
## 28 1888.3633 219723.43750
## 29 1886.8047 219513.42188
## 30 1885.2441 219388.48438
## 31 1883.6855 218151.32812
## 32 1882.1250 219419.18750
## 33 1880.5645 218475.73438
## 34 1879.0039 218705.12500
## 35 1877.4414 218449.20312
## 36 1875.8809 218343.90625
## 37 1874.3184 218529.95312
## 38 1872.7559 217712.57812
## 39 1871.1934 217732.31250
## 40 1869.6309 218025.56250
## 41 1868.0664 218026.03125
## 42 1866.5039 216992.04688
## 43 1864.9395 216661.85938
## 44 1863.3750 216434.10938
## 45 1861.8105 217021.34375
## 46 1860.2461 216859.10938
## 47 1858.6797 215777.01562
## 48 1857.1152 216036.68750
## 49 1855.5488 216008.87500
## 50 1853.9824 215570.90625
## 51 1852.4160 216049.07812
## 52 1850.8496 215721.37500
## 53 1849.2812 215108.29688
## 54 1847.7129 214241.93750
## 55 1846.1465 215022.92188
## 56 1844.5781 215059.15625
## 57 1843.0078 214354.34375
## 58 1841.4395 214081.62500
## 59 1839.8711 213777.15625
## 60 1838.3008 214527.14062
## 61 1836.7305 214412.20312
## 62 1835.1602 214236.82812
## 63 1833.5898 213779.81250
## 64 1832.0176 212764.46875
## 65 1830.4473 212580.01562
## 66 1828.8750 213229.10938
## 67 1827.3027 212779.07812
## 68 1825.7305 212281.54688
## 69 1824.1582 212102.12500
## 70 1822.5840 211949.81250
## 71 1821.0117 211049.21875
## 72 1819.4375 210789.18750
## 73 1817.8633 211080.34375
## 74 1816.2891 210685.45312
## 75 1814.7129 209914.51562
## 76 1813.1387 209939.03125
## 77 1811.5625 210293.60938
## 78 1809.9863 209775.48438
## 79 1808.4102 209577.98438
## 80 1806.8340 209356.78125
## 81 1805.2578 209218.10938
## 82 1803.6797 209474.17188
## 83 1802.1016 209326.03125
## 84 1800.5254 207991.25000
## 85 1798.9473 208312.35938
## 86 1797.3672 207955.51562
## 87 1795.7891 208571.01562
## 88 1794.2090 208165.18750
## 89 1792.6309 207675.65625
## 90 1791.0508 208433.21875
## 91 1789.4707 207165.50000
## 92 1787.8887 206799.87500
## 93 1786.3086 206726.93750
## 94 1784.7266 207033.46875
## 95 1783.1445 207115.42188
## 96 1781.5625 206614.20312
## 97 1779.9805 206437.09375
## 98 1778.3984 206585.46875
## 99 1776.8145 206868.01562
## 100 1775.2324 205972.53125
## 101 1773.6484 206019.87500
## 102 1772.0645 206217.12500
## 103 1770.4805 205394.95312
## 104 1768.8945 205469.18750
## 105 1767.3105 205691.67188
## 106 1765.7246 204794.43750
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## 108 1762.5527 205223.60938
## 109 1760.9668 204218.31250
## 110 1759.3789 204059.51562
## 111 1757.7930 204288.29688
## 112 1756.2051 204523.28125
## 113 1754.6172 204156.67188
## 114 1753.0293 204265.62500
## 115 1751.4414 203211.45312
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## 119 1745.0840 202851.90625
## 120 1743.4941 202987.71875
## 121 1741.9023 202505.73438
## 122 1740.3125 203125.57812
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## 125 1735.5371 202194.29688
## 126 1733.9453 201990.89062
## 127 1732.3535 201657.28125
## 128 1730.7598 201722.46875
## 129 1729.1680 201406.31250
## 130 1727.5742 201476.20312
## 131 1725.9805 200427.75000
## 132 1724.3848 200776.95312
## 133 1722.7910 200748.15625
## 134 1721.1953 200443.50000
## 135 1719.6016 200088.82812
## 136 1718.0059 200483.21875
## 137 1716.4082 200034.68750
## 138 1714.8125 200156.40625
## 139 1713.2168 199910.10938
## 140 1711.6191 199479.20312
## 141 1710.0215 200918.79688
## 142 1708.4238 199550.51562
## 143 1706.8262 199410.92188
## 144 1705.2285 198841.25000
## 145 1703.6289 199358.65625
## 146 1702.0312 199422.43750
## 147 1700.4316 198624.89062
## 148 1698.8320 197896.45312
## 149 1697.2305 198540.15625
## 150 1695.6309 197827.64062
## 151 1694.0293 197671.43750
## 152 1692.4297 198121.14062
## 153 1690.8281 197076.64062
## 154 1689.2266 197474.76562
## 155 1687.6230 196977.00000
## 156 1686.0215 197524.73438
## 157 1684.4180 197211.21875
## 158 1682.8164 196568.78125
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## 161 1678.0039 196973.00000
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## 167 1668.3730 195818.54688
## 168 1666.7656 196433.53125
## 169 1665.1582 195994.93750
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## 171 1661.9434 196393.54688
## 172 1660.3359 196661.12500
## 173 1658.7285 196096.93750
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## 175 1655.5098 196465.57812
## 176 1653.9004 196148.76562
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## 179 1649.0723 197416.09375
## 180 1647.4609 196767.15625
## 181 1645.8496 196635.25000
## 182 1644.2383 196964.18750
## 183 1642.6270 197266.62500
## 184 1641.0156 197556.51562
## 185 1639.4023 197357.84375
## 186 1637.7891 198128.12500
## 187 1636.1758 197946.46875
## 188 1634.5625 198119.70312
## 189 1632.9492 198027.95312
## 190 1631.3359 198246.01562
## 191 1629.7207 198510.42188
## 192 1628.1055 198575.00000
## 193 1626.4922 199795.79688
## 194 1624.8750 199199.25000
## 195 1623.2598 199600.89062
## 196 1621.6445 199612.54688
## 197 1620.0273 200315.50000
## 198 1618.4102 200634.68750
## 199 1616.7930 200332.26562
## 200 1615.1758 200711.56250
## 201 1613.5586 200407.68750
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## 205 1607.0840 201827.35938
## 206 1605.4629 202420.37500
## 207 1603.8438 201650.39062
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## 209 1600.6035 202670.60938
## 210 1598.9824 202297.40625
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## 215 1590.8711 202270.10938
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## 217 1587.6250 202850.81250
## 218 1586.0020 201683.65625
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## 221 1581.1289 202044.14062
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## 231 1564.8613 199865.37500
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## 235 1558.3457 198885.07812
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## 261 1515.8535 190527.76562
## 262 1514.2148 189562.76562
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## 266 1507.6543 188651.07812
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## 900 390.5742 68355.91406
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## 904 382.9941 68348.96875
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## 983 231.8594 59689.60938
## 984 229.9277 59396.32422
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## 986 226.0645 59339.50000
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## 999 200.9141 57987.66016
## 1000 198.9766 58011.93750
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## 1002 195.0996 57927.64844
## 1003 193.1602 57706.97656
## 1004 191.2207 57273.72656
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## 1008 183.4570 57166.61719
## 1009 181.5156 57312.08203
## 1010 179.5742 57088.05469
## 1011 177.6309 57062.31641
## 1012 175.6875 56682.76562
## 1013 173.7441 56735.60156
## 1014 171.8008 56828.28516
## 1015 169.8574 56351.33594
## 1016 167.9121 56333.51953
## 1017 165.9668 56615.16016
## 1018 164.0215 56142.44531
## 1019 162.0742 56249.30469
## 1020 160.1289 55870.64062
## 1021 158.1816 56086.66406
## 1022 156.2344 55929.01953
## 1023 154.2852 56011.45703
## 1024 152.3379 1319.10071
## 1025 150.3887 50.03250
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## 1027 146.4902 24.36390
## 1028 144.5391 25.64047
## 1029 142.5898 30.76169
## 1030 140.6387 32.03627
## 1031 138.6875 11.53048
##
## [[4]]
## Raman_shift Counts
## 1 1930.3320 14582.61133
## 2 1928.7832 14480.99609
## 3 1927.2324 14600.35449
## 4 1925.6816 14500.39844
## 5 1924.1309 14722.86133
## 6 1922.5801 14561.68457
## 7 1921.0273 14550.41797
## 8 1919.4746 14516.60644
## 9 1917.9238 14651.90723
## 10 1916.3711 14519.87207
## 11 1914.8164 14632.57715
## 12 1913.2637 14614.87305
## 13 1911.7109 14487.76269
## 14 1910.1562 14510.31543
## 15 1908.6016 14492.65039
## 16 1907.0469 14526.45019
## 17 1905.4922 14613.28613
## 18 1903.9355 14362.55762
## 19 1902.3809 14595.62207
## 20 1900.8242 14467.10742
## 21 1899.2676 14439.84082
## 22 1897.7109 14427.04004
## 23 1896.1543 14586.03418
## 24 1894.5977 14451.21582
## 25 1893.0391 14513.84668
## 26 1891.4805 14509.06445
## 27 1889.9219 14193.08887
## 28 1888.3633 14510.73438
## 29 1886.8047 14396.92578
## 30 1885.2441 14295.99121
## 31 1883.6855 14283.25000
## 32 1882.1250 14534.90234
## 33 1880.5645 14432.40430
## 34 1879.0039 14461.28516
## 35 1877.4414 14490.15430
## 36 1875.8809 14402.14258
## 37 1874.3184 14338.17773
## 38 1872.7559 14312.64648
## 39 1871.1934 14263.12891
## 40 1869.6309 14466.36328
## 41 1868.0664 14316.08398
## 42 1866.5039 14469.65430
## 43 1864.9395 14372.19141
## 44 1863.3750 14295.54785
## 45 1861.8105 14476.18457
## 46 1860.2461 14284.53516
## 47 1858.6797 14398.01758
## 48 1857.1152 14452.37109
## 49 1855.5488 14275.21973
## 50 1853.9824 14356.70996
## 51 1852.4160 14458.90918
## 52 1850.8496 14269.10938
## 53 1849.2812 14360.11914
## 54 1847.7129 14203.91211
## 55 1846.1465 14234.31055
## 56 1844.5781 14226.44141
## 57 1843.0078 14260.00977
## 58 1841.4395 14231.42578
## 59 1839.8711 14304.80273
## 60 1838.3008 14328.77832
## 61 1836.7305 14233.32227
## 62 1835.1602 14325.75586
## 63 1833.5898 14238.29981
## 64 1832.0176 14354.56055
## 65 1830.4473 14166.91113
## 66 1828.8750 14162.25488
## 67 1827.3027 14241.88281
## 68 1825.7305 14238.80273
## 69 1824.1582 14068.82519
## 70 1822.5840 14100.74707
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Plot spectral analysis
library(ggpubr)
test2plot <- ggplot(data = test2_10_8)+
geom_path(aes(x = Raman_shift, y = Counts))
testcontrolplot <- ggplot(data = test_control)+
geom_path(aes(x = Raman_shift, y = Counts))
potplastplot <- ggplot(data = pot_plastic)+
geom_path(aes(x = Raman_shift, y = Counts))
pot1plastplot <- ggplot(data = pot_plastic1)+
geom_path(aes(x = Raman_shift, y = Counts))
testcontrolplot
test2plot
ggarrange(test2plot, testcontrolplot, potplastplot, pot1plastplot)
setwd("/Users/christophercarnivale/Desktop/Dissertation_data/Raman Spectral Data copy/9_18_19_spectral_data/")
#list.files()
#plot for white filter control w/oil
oil_control <- read.delim("renB531_control_w_oil_0__Time_0.txt", header = F, sep = "\t")
colnames(oil_control) <- c("Raman_shift", "Counts")
test_control <- as.data.frame(oil_control)
oilcontrolplot <- ggplot(data = oil_control)+
geom_path(aes(x = Raman_shift, y = Counts))
oilcontrolplot
#plot of individual microspheres on white filter w/oil
micro_indv <- read.delim("renD6C5_microsphere_ind_0__Time_0.txt", header = F, sep = "\t")
colnames(micro_indv) <- c("Raman_shift", "Counts")
micro_indv <- as.data.frame(micro_indv)
micro_indv_plot <- ggplot(micro_indv, aes(x = Raman_shift, y = Counts))+
geom_path()
micro_indv_plot
micro_indv1 <- read.delim("renC2B9_microsphere_ind1_0__Time_0.txt", header = F, sep = "\t")
colnames(micro_indv1) <- c("Raman_shift", "Counts")
micro_indv1 <- as.data.frame(micro_indv1)
micro_indv1_plot <- ggplot(micro_indv1, aes(x = Raman_shift, y = Counts))+
geom_path()
micro_indv1_plot
micro_indv2 <- read.delim("ren3CA9_microsphere_ind2_0__Time_0.txt", header = F, sep = "\t")
colnames(micro_indv2) <- c("Raman_shift", "Counts")
micro_indv2 <- as.data.frame(micro_indv2)
micro_indv2_plot <- ggplot(micro_indv2, aes(x = Raman_shift, y = Counts))+
geom_path()
micro_indv2_plot
#Now reading and plotting with single function using sourced function
micro_indv3_plot <- Raman_plot_function("ren7832_microsphere_ind3_0__Time_0.txt")
micro_indv3_plot
micro_indv4_plot <- Raman_plot_function("ren3359_microsphere_spec_0__Time_0.txt")
micro_indv4_plot
#Black filter
pot_plastic_st4 <- Raman_plot_function("renE0D5a_st4_potplastic_0__Time_0.txt")
pot_plastic_st4
#White filter clusters of microspheres
#Plot of all indv microsphere plots
ggarrange(oilcontrolplot, micro_indv_plot, micro_indv2_plot, micro_indv3_plot)
Boilcontrolplot <- Raman_plot_function("renED08Z_blackfilter_control_wOIL_0__Time_0.txt")
Boilcontrolplot
#Black filter plot
ggarrange(Boilcontrolplot, pot_plastic_st4)
setwd("/Users/christophercarnivale/Desktop/Dissertation_data/Raman Spectral Data copy/9_18_19_spectral_data/")
micro_indv_pic <- readPNG("microsphere_w_scalebar_ind3.PNG")
micro_indv_pic <- as.raster(micro_indv_pic)
plot(micro_indv_pic)
micro_indv1_pic <- read.bmp("microsphere1.bmp")
micro_indv1_pic <- pixmapRGB(micro_indv1_pic)
plot(micro_indv1_pic)
#baselineGUI() need to install gWidgets to use this functionality
str(milk)
## 'data.frame': 45 obs. of 2 variables:
## $ cow : num 0 0.25 0.375 0.875 0.5 0.75 0.5 0.125 0 0.125 ...
## $ spectra: num [1:45, 1:21451] 1029 371 606 368 554 ...
## ..- attr(*, "dimnames")=List of 2
## .. ..$ : NULL
## .. ..$ : chr [1:21451] "4999.94078628963" "5001.55954267662" "5003.17856106153" "5004.79784144435" ...
## - attr(*, "terms")=Classes 'terms', 'formula' language cow ~ spectra
## .. ..- attr(*, "variables")= language list(cow, spectra)
## .. ..- attr(*, "factors")= int [1:2, 1] 0 1
## .. .. ..- attr(*, "dimnames")=List of 2
## .. .. .. ..$ : chr [1:2] "cow" "spectra"
## .. .. .. ..$ : chr "spectra"
## .. ..- attr(*, "term.labels")= chr "spectra"
## .. ..- attr(*, "order")= int 1
## .. ..- attr(*, "intercept")= int 1
## .. ..- attr(*, "response")= int 1
## .. ..- attr(*, ".Environment")=<environment: R_GlobalEnv>
## .. ..- attr(*, "predvars")= language list(cow, spectra)
## .. ..- attr(*, "dataClasses")= Named chr [1:2] "numeric" "nmatrix.21451"
## .. .. ..- attr(*, "names")= chr [1:2] "cow" "spectra"
#View(milk$spectra[1,,drop = F])
test_control_polymod <- read.delim("renB232_0__Time_0_test_control.txt", header = F, sep = "\t")
#head(test_control_polymod)
test_control_polymod1 <- t(test_control_polymod)
#View(test_control_polymod1)
colnames(test_control_polymod1) <- test_control_polymod1[1,]
test_control_polymod2 <- test_control_polymod1[-1,]
#View(test_control_polymod2)
test_control_polymod3 <- t(test_control_polymod2)
#View(test_control_polymod3)
test_control_polymod4 <- baseline.modpolyfit(test_control_polymod3, degree = 2)
#View(test_control_polymod4)
#View(test_control_polymod4$baseline)
#View(test_control_polymod4$corrected)
#the plotting works using the S4 type data brom the baseline function
#however, that isn't easy to work with and hard to extract the corrected plot
#I need to figure a way to plot the data to and extract the plot so as to
#compare to the library data.
data(milk)
#The way the functions are used have changed and some arguments are no longer used.
#ANY object using baseline() will be broken and I dont need to fix
#bc.modpolyfit <- baseline.modpolyfit(milk$spectra[1,, drop=FALSE], method='modpolyfit', deg=3)
#plot(bc.modpolyfit)
##View(bc.modpolyfit@baseline)
#test_control_polymod5 <- baseline(test_control_polymod1, method = 'modpolyfit', deg = 3)
#plot(test_control_polymod5)
#test_control_polymod6 <- baseline(test_control_polymod3, method = 'modpolyfit', deg = 6)
#plot(test_control_polymod6)
#plot(test_control_polymod6@spectra)
#View(test_control_polymod6@spectra)
#View(test_control_polymod6@corrected)
#View(test_control_polymod6@baseline)
setwd("/Users/christophercarnivale/Desktop/Dissertation_data/Raman Spectral Data copy/9_18_19_spectral_data/")
#test_micro_indv_polymod_df <- Raman_df_function("ren3CA9_microsphere_ind2_0__Time_0.txt")
#test_micro_indv_polymod_df_transpose <- t(test_micro_indv_polymod_df)
#colnames(test_micro_indv_polymod_df) <- test_micro_indv_polymod_df[1,]
#test
#class(test_control_polymod6)
#showMethods(class = "baseline")
#method ? plot("baseline")
#test_micro_indv_polymod_df
#baseline can't be converted into data frame so can't use ggplot2
#Three separate packages have baseline corrections using basically the same algorithm
#prospectr::baselines(), Baseline::baseline.modpolyfit(), hyperSpec::spc.fit.poly()
#prospectr is the only one that does it baseline correction differently and not the common way so I will not use that. The hyperSpec is made to be used with hyperspec obj so I will continue with that one. Currently the
setwd("/Users/christophercarnivale/Desktop/Dissertation_data/Raman Spectral Data copy/10_17_19_spectral_data/")
Bfilter_sample4_plot <- Raman_plot_function("ren68C2_Bfilter_sample4_fullrange_0__Time_0.txt")
Bfilter_sample4_plot
Bfilter_sample5_plot <- Raman_plot_function("ren41A6_Bfilter_sample5_fullrange_0__Time_0.txt")
Bfilter_sample5_plot
Bfilter_control_plot <- Raman_plot_function("renF6B0_Bfilter_rerun_control_0__Time_0.txt")
Bfilter_control_plot
Baseline package is a bust and difficult to work with with no method for resolving baseline objects…seems like hyperspec has more flexibilty
setwd("/Users/christophercarnivale/Desktop/Dissertation_data/Raman Spectral Data copy/10_24_19_spectral_data/")
potP14plot <- Raman_plot_function("renEFCC_Bfilter_potP14_0__Time_0.txt")
potP14plot
#baselines <- spc.fit.poly.below(test_control_polymod3)
#need to convert to hyperspec object first
plot(chondro)
#convert to hyperspec object
hyper_test <- as.hyperSpec(test_control_polymod3)
plot(hyper_test)
#Baselines
baselines <- spc.fit.poly.below(hyper_test, poly.order = 6)
#Plotting baseline corrections
plot(hyper_test-baselines)
#Notes on hyperspec objects and their structure.
S4 objects with an S3 data frame object within it. Assumes that a data frame is providing “meta data” *matrix is assumed to be the spectral data
For my larger data sets I need to form a list with extra data (environmental) and internal matrix with all of the spectra for a given sample. Ideally I would have all spectra for a sample within 1 matrix for identification purposes.
I have created a basic hyperspec with 1 spectra above now… Can I apply baseline corrections into hyper_test data? From there I can do the standard normal variate correction and min-max standardization. Then I could finally take this data to compare to the library.
hyper_baseline_corr <- hyper_test-baselines
plot(hyper_baseline_corr)
#Now that I have baseline corrected data I can normalize and min max standardize
hyper_baseline_corr@data[["spc"]] <- standardNormalVariate(hyper_baseline_corr@data[["spc"]])
hyper_baseline_corr@data[["spc"]] <- (hyper_baseline_corr@data[["spc"]] - min(hyper_baseline_corr@data[["spc"]])) / (max(hyper_baseline_corr@data[["spc"]])-min(hyper_baseline_corr@data[["spc"]]))
plot(hyper_baseline_corr)
In the previous chunk, I was able to baseline correct, standard variate normalize and min-max scale the spectra to be able to compare to a library. Next step is to import the reference library to compare spectra. Then I can do correlations to determine the likely polymer type.
I also need to alter the the original data set to be in the same matter as the reference library. The library is a 1 wavelength step the current data I have approx to a 2 wavelength step between values. Need to use the approxfun() to interpolate on the frequency index they used to have the exact same number of values for the eventual correlation matrix. The frequency goes from 780 min to 1750 max by steps of 1. Need to subset my initial data according to this.
ref_lib <- read.csv("/Users/christophercarnivale/Desktop/Dissertation_data/spectra_db.csv")
str(ref_lib)
## 'data.frame': 451515 obs. of 8 variables:
## $ X : int 1262413 1262513 1262613 1262713 1262813 1262913 1263013 1263113 1263213 1263313 ...
## $ ID : chr "ABS 1" "ABS 1" "ABS 1" "ABS 1" ...
## $ SAMPLE : chr "ABS" "ABS" "ABS" "ABS" ...
## $ INTENSITY: num 0.119 0.114 0.11 0.113 0.109 ...
## $ WAVE : int 780 781 782 783 784 785 786 787 788 789 ...
## $ PARTICLE : chr "1" "1" "1" "1" ...
## $ SOURCE : chr "plastic" "plastic" "plastic" "plastic" ...
## $ poly_lab : chr "ABS" "ABS" "ABS" "ABS" ...
plastic_ref_lib <- ref_lib %>%
dplyr::filter(SOURCE == "plastic")
#create aprrox wavelength from particle data to get the same number of data
#points for a correlation analysis
freq_index <- seq(780, 1750, by = 1)
plastic_approx_fun <- approxfun(x = hyper_baseline_corr@wavelength, hyper_baseline_corr@data[["spc"]])
#test approx
plastic_approx <- plastic_approx_fun(freq_index)
cbind(freq_index, plastic_approx)
## freq_index plastic_approx
## [1,] 780 0.0072165391
## [2,] 781 0.0065858939
## [3,] 782 0.0065644072
## [4,] 783 0.0068442473
## [5,] 784 0.0072408617
## [6,] 785 0.0070251642
## [7,] 786 0.0068388549
## [8,] 787 0.0072754733
## [9,] 788 0.0075854061
## [10,] 789 0.0076216350
## [11,] 790 0.0077366558
## [12,] 791 0.0079256932
## [13,] 792 0.0085413678
## [14,] 793 0.0093272308
## [15,] 794 0.0107410908
## [16,] 795 0.0120459996
## [17,] 796 0.0120963881
## [18,] 797 0.0126662281
## [19,] 798 0.0143583412
## [20,] 799 0.0150228073
## [21,] 800 0.0147368923
## [22,] 801 0.0166086599
## [23,] 802 0.0193166032
## [24,] 803 0.0189128285
## [25,] 804 0.0184728911
## [26,] 805 0.0196257495
## [27,] 806 0.0210115732
## [28,] 807 0.0228744213
## [29,] 808 0.0256721762
## [30,] 809 0.0292948487
## [31,] 810 0.0333401026
## [32,] 811 0.0375366789
## [33,] 812 0.0388921344
## [34,] 813 0.0405214952
## [35,] 814 0.0448558852
## [36,] 815 0.0496888982
## [37,] 816 0.0554495188
## [38,] 817 0.0609914138
## [39,] 818 0.0663577118
## [40,] 819 0.0713293429
## [41,] 820 0.0761775143
## [42,] 821 0.0849160360
## [43,] 822 0.0936216816
## [44,] 823 0.1015360616
## [45,] 824 0.1091735488
## [46,] 825 0.1163609069
## [47,] 826 0.1247434476
## [48,] 827 0.1339570746
## [49,] 828 0.1451415831
## [50,] 829 0.1568404234
## [51,] 830 0.1705669387
## [52,] 831 0.1837704138
## [53,] 832 0.1950624172
## [54,] 833 0.2080540267
## [55,] 834 0.2233561618
## [56,] 835 0.2401616882
## [57,] 836 0.2578395547
## [58,] 837 0.2731775184
## [59,] 838 0.2884258066
## [60,] 839 0.3096142115
## [61,] 840 0.3291962795
## [62,] 841 0.3443372985
## [63,] 842 0.3638000511
## [64,] 843 0.3880096355
## [65,] 844 0.4065765197
## [66,] 845 0.4225785401
## [67,] 846 0.4372506776
## [68,] 847 0.4520741462
## [69,] 848 0.4692337962
## [70,] 849 0.4837267607
## [71,] 850 0.4927107073
## [72,] 851 0.4968237900
## [73,] 852 0.4967719227
## [74,] 853 0.4982258269
## [75,] 854 0.5001764406
## [76,] 855 0.4975764567
## [77,] 856 0.4943328416
## [78,] 857 0.4888935429
## [79,] 858 0.4827113348
## [80,] 859 0.4753982924
## [81,] 860 0.4662572300
## [82,] 861 0.4559539433
## [83,] 862 0.4446179268
## [84,] 863 0.4330297593
## [85,] 864 0.4206377020
## [86,] 865 0.4081079506
## [87,] 866 0.3951897788
## [88,] 867 0.3848613242
## [89,] 868 0.3773551287
## [90,] 869 0.3651867898
## [91,] 870 0.3509754843
## [92,] 871 0.3387120805
## [93,] 872 0.3270258951
## [94,] 873 0.3179571226
## [95,] 874 0.3070617927
## [96,] 875 0.2927684484
## [97,] 876 0.2819255675
## [98,] 877 0.2736823561
## [99,] 878 0.2622456065
## [100,] 879 0.2499893815
## [101,] 880 0.2412054837
## [102,] 881 0.2317411677
## [103,] 882 0.2200049503
## [104,] 883 0.2092070991
## [105,] 884 0.1995507233
## [106,] 885 0.1904241037
## [107,] 886 0.1815562022
## [108,] 887 0.1772974269
## [109,] 888 0.1733600782
## [110,] 889 0.1676315847
## [111,] 890 0.1631764030
## [112,] 891 0.1612163193
## [113,] 892 0.1589367497
## [114,] 893 0.1564067121
## [115,] 894 0.1559942127
## [116,] 895 0.1561472320
## [117,] 896 0.1536796561
## [118,] 897 0.1525293073
## [119,] 898 0.1557291254
## [120,] 899 0.1568372267
## [121,] 900 0.1554602944
## [122,] 901 0.1559767148
## [123,] 902 0.1573774216
## [124,] 903 0.1566299781
## [125,] 904 0.1557147472
## [126,] 905 0.1551549473
## [127,] 906 0.1540074548
## [128,] 907 0.1518034008
## [129,] 908 0.1492501929
## [130,] 909 0.1464482561
## [131,] 910 0.1461697740
## [132,] 911 0.1464075550
## [133,] 912 0.1450199292
## [134,] 913 0.1443595946
## [135,] 914 0.1456900219
## [136,] 915 0.1444672248
## [137,] 916 0.1406306566
## [138,] 917 0.1379149864
## [139,] 918 0.1356294991
## [140,] 919 0.1345441604
## [141,] 920 0.1336839951
## [142,] 921 0.1335723950
## [143,] 922 0.1307122744
## [144,] 923 0.1239312723
## [145,] 924 0.1184736475
## [146,] 925 0.1137521266
## [147,] 926 0.1087081087
## [148,] 927 0.1035100887
## [149,] 928 0.0973844886
## [150,] 929 0.0925211436
## [151,] 930 0.0901749313
## [152,] 931 0.0856162674
## [153,] 932 0.0793639319
## [154,] 933 0.0748158585
## [155,] 934 0.0707845170
## [156,] 935 0.0694669426
## [157,] 936 0.0674814586
## [158,] 937 0.0636307769
## [159,] 938 0.0608795308
## [160,] 939 0.0592363430
## [161,] 940 0.0556871718
## [162,] 941 0.0514416817
## [163,] 942 0.0504443149
## [164,] 943 0.0496649481
## [165,] 944 0.0493082751
## [166,] 945 0.0487605864
## [167,] 946 0.0479653484
## [168,] 947 0.0468915133
## [169,] 948 0.0456816192
## [170,] 949 0.0450556849
## [171,] 950 0.0445756616
## [172,] 951 0.0446098186
## [173,] 952 0.0423500285
## [174,] 953 0.0363300532
## [175,] 954 0.0361547053
## [176,] 955 0.0396045138
## [177,] 956 0.0397836004
## [178,] 957 0.0393610047
## [179,] 958 0.0385392290
## [180,] 959 0.0374424953
## [181,] 960 0.0357877103
## [182,] 961 0.0355138507
## [183,] 962 0.0362887368
## [184,] 963 0.0366513796
## [185,] 964 0.0370240065
## [186,] 965 0.0395838515
## [187,] 966 0.0409862394
## [188,] 967 0.0395146641
## [189,] 968 0.0410501097
## [190,] 969 0.0452807344
## [191,] 970 0.0466481834
## [192,] 971 0.0471590796
## [193,] 972 0.0487259634
## [194,] 973 0.0506709740
## [195,] 974 0.0537503652
## [196,] 975 0.0561788854
## [197,] 976 0.0579358707
## [198,] 977 0.0603443950
## [199,] 978 0.0629885824
## [200,] 979 0.0638345612
## [201,] 980 0.0648674493
## [202,] 981 0.0666997157
## [203,] 982 0.0683702311
## [204,] 983 0.0698545290
## [205,] 984 0.0696394182
## [206,] 985 0.0687267527
## [207,] 986 0.0728300661
## [208,] 987 0.0760318712
## [209,] 988 0.0745613265
## [210,] 989 0.0743563339
## [211,] 990 0.0757528823
## [212,] 991 0.0777916826
## [213,] 992 0.0801224156
## [214,] 993 0.0802107946
## [215,] 994 0.0799761939
## [216,] 995 0.0789952577
## [217,] 996 0.0788996112
## [218,] 997 0.0799978887
## [219,] 998 0.0809865614
## [220,] 999 0.0819221792
## [221,] 1000 0.0845830035
## [222,] 1001 0.0866512065
## [223,] 1002 0.0851102084
## [224,] 1003 0.0852018891
## [225,] 1004 0.0875671108
## [226,] 1005 0.0874090490
## [227,] 1006 0.0859856478
## [228,] 1007 0.0862527769
## [229,] 1008 0.0862791434
## [230,] 1009 0.0842689245
## [231,] 1010 0.0821130112
## [232,] 1011 0.0797509260
## [233,] 1012 0.0781195306
## [234,] 1013 0.0768577791
## [235,] 1014 0.0772940352
## [236,] 1015 0.0781786565
## [237,] 1016 0.0805335856
## [238,] 1017 0.0812204354
## [239,] 1018 0.0795844480
## [240,] 1019 0.0788297321
## [241,] 1020 0.0785092283
## [242,] 1021 0.0776910627
## [243,] 1022 0.0773664470
## [244,] 1023 0.0795880301
## [245,] 1024 0.0821022913
## [246,] 1025 0.0850081269
## [247,] 1026 0.0857653230
## [248,] 1027 0.0855190015
## [249,] 1028 0.0875200530
## [250,] 1029 0.0896673517
## [251,] 1030 0.0917779342
## [252,] 1031 0.0936395626
## [253,] 1032 0.0951886270
## [254,] 1033 0.0972809814
## [255,] 1034 0.0996070412
## [256,] 1035 0.1028146600
## [257,] 1036 0.1052276533
## [258,] 1037 0.1044688430
## [259,] 1038 0.1049335069
## [260,] 1039 0.1067956950
## [261,] 1040 0.1088316668
## [262,] 1041 0.1109343341
## [263,] 1042 0.1097717733
## [264,] 1043 0.1092033888
## [265,] 1044 0.1107652121
## [266,] 1045 0.1118837610
## [267,] 1046 0.1125485235
## [268,] 1047 0.1127341906
## [269,] 1048 0.1127368702
## [270,] 1049 0.1106356398
## [271,] 1050 0.1106412657
## [272,] 1051 0.1163567845
## [273,] 1052 0.1191264517
## [274,] 1053 0.1192763778
## [275,] 1054 0.1189595716
## [276,] 1055 0.1187023028
## [277,] 1056 0.1220353574
## [278,] 1057 0.1260233913
## [279,] 1058 0.1314655610
## [280,] 1059 0.1357757727
## [281,] 1060 0.1392331376
## [282,] 1061 0.1426136542
## [283,] 1062 0.1457272216
## [284,] 1063 0.1464102489
## [285,] 1064 0.1483287756
## [286,] 1065 0.1524874909
## [287,] 1066 0.1566744303
## [288,] 1067 0.1608787313
## [289,] 1068 0.1630984595
## [290,] 1069 0.1650156485
## [291,] 1070 0.1667309140
## [292,] 1071 0.1682331329
## [293,] 1072 0.1694264851
## [294,] 1073 0.1728808212
## [295,] 1074 0.1774393586
## [296,] 1075 0.1773506444
## [297,] 1076 0.1773265790
## [298,] 1077 0.1787894682
## [299,] 1078 0.1799636449
## [300,] 1079 0.1808080372
## [301,] 1080 0.1825624944
## [302,] 1081 0.1846494284
## [303,] 1082 0.1905264108
## [304,] 1083 0.1941259132
## [305,] 1084 0.1913446343
## [306,] 1085 0.1908876052
## [307,] 1086 0.1924813848
## [308,] 1087 0.1945160702
## [309,] 1088 0.1964935765
## [310,] 1089 0.1961061799
## [311,] 1090 0.1956312658
## [312,] 1091 0.1949818281
## [313,] 1092 0.1925745744
## [314,] 1093 0.1890030506
## [315,] 1094 0.1908372113
## [316,] 1095 0.1924066484
## [317,] 1096 0.1874697926
## [318,] 1097 0.1850004649
## [319,] 1098 0.1860796813
## [320,] 1099 0.1870949228
## [321,] 1100 0.1880800153
## [322,] 1101 0.1881564709
## [323,] 1102 0.1885026338
## [324,] 1103 0.1899727676
## [325,] 1104 0.1903443639
## [326,] 1105 0.1895825462
## [327,] 1106 0.1942708574
## [328,] 1107 0.2005487721
## [329,] 1108 0.2041643334
## [330,] 1109 0.2086454701
## [331,] 1110 0.2150837960
## [332,] 1111 0.2191214937
## [333,] 1112 0.2214213619
## [334,] 1113 0.2286459544
## [335,] 1114 0.2363119227
## [336,] 1115 0.2410584309
## [337,] 1116 0.2456787378
## [338,] 1117 0.2501100507
## [339,] 1118 0.2593811178
## [340,] 1119 0.2709949079
## [341,] 1120 0.2720474299
## [342,] 1121 0.2733988287
## [343,] 1122 0.2778463205
## [344,] 1123 0.2816704730
## [345,] 1124 0.2848663977
## [346,] 1125 0.2871516321
## [347,] 1126 0.2886355050
## [348,] 1127 0.2801265548
## [349,] 1128 0.2752808119
## [350,] 1129 0.2780027684
## [351,] 1130 0.2775008498
## [352,] 1131 0.2748809842
## [353,] 1132 0.2721974269
## [354,] 1133 0.2688546170
## [355,] 1134 0.2620497681
## [356,] 1135 0.2565388534
## [357,] 1136 0.2527048300
## [358,] 1137 0.2462203160
## [359,] 1138 0.2386785036
## [360,] 1139 0.2314078841
## [361,] 1140 0.2243053300
## [362,] 1141 0.2176494840
## [363,] 1142 0.2153930647
## [364,] 1143 0.2166966109
## [365,] 1144 0.2144098361
## [366,] 1145 0.2117980784
## [367,] 1146 0.2123779106
## [368,] 1147 0.2120238504
## [369,] 1148 0.2102248116
## [370,] 1149 0.2089829139
## [371,] 1150 0.2080107049
## [372,] 1151 0.2050433638
## [373,] 1152 0.2025910790
## [374,] 1153 0.2021063889
## [375,] 1154 0.2017994085
## [376,] 1155 0.2016567763
## [377,] 1156 0.1971286395
## [378,] 1157 0.1915441586
## [379,] 1158 0.1863379939
## [380,] 1159 0.1814428256
## [381,] 1160 0.1770836310
## [382,] 1161 0.1736967812
## [383,] 1162 0.1708281264
## [384,] 1163 0.1627529877
## [385,] 1164 0.1558212369
## [386,] 1165 0.1541932955
## [387,] 1166 0.1481456841
## [388,] 1167 0.1377807824
## [389,] 1168 0.1301942236
## [390,] 1169 0.1234215593
## [391,] 1170 0.1176175090
## [392,] 1171 0.1111393142
## [393,] 1172 0.1034695228
## [394,] 1173 0.0988116719
## [395,] 1174 0.0957868043
## [396,] 1175 0.0869950050
## [397,] 1176 0.0786388586
## [398,] 1177 0.0731907568
## [399,] 1178 0.0688775028
## [400,] 1179 0.0656556141
## [401,] 1180 0.0577637309
## [402,] 1181 0.0488518683
## [403,] 1182 0.0417525743
## [404,] 1183 0.0356548365
## [405,] 1184 0.0312263589
## [406,] 1185 0.0247131905
## [407,] 1186 0.0171454114
## [408,] 1187 0.0146957714
## [409,] 1188 0.0119977503
## [410,] 1189 0.0077349185
## [411,] 1190 0.0053843938
## [412,] 1191 0.0047080144
## [413,] 1192 0.0031415477
## [414,] 1193 0.0015042958
## [415,] 1194 0.0007810596
## [416,] 1195 0.0002680486
## [417,] 1196 0.0000622901
## [418,] 1197 0.0005955999
## [419,] 1198 0.0014497932
## [420,] 1199 0.0050323977
## [421,] 1200 0.0081037395
## [422,] 1201 0.0097997554
## [423,] 1202 0.0134773714
## [424,] 1203 0.0186243355
## [425,] 1204 0.0202503996
## [426,] 1205 0.0215250958
## [427,] 1206 0.0237708397
## [428,] 1207 0.0254013077
## [429,] 1208 0.0262950285
## [430,] 1209 0.0252649387
## [431,] 1210 0.0237278110
## [432,] 1211 0.0250445725
## [433,] 1212 0.0267145255
## [434,] 1213 0.0290705624
## [435,] 1214 0.0287616734
## [436,] 1215 0.0269194295
## [437,] 1216 0.0246653948
## [438,] 1217 0.0225712714
## [439,] 1218 0.0211262026
## [440,] 1219 0.0179460311
## [441,] 1220 0.0131861363
## [442,] 1221 0.0141916649
## [443,] 1222 0.0159669864
## [444,] 1223 0.0140275005
## [445,] 1224 0.0125629950
## [446,] 1225 0.0117652450
## [447,] 1226 0.0110738195
## [448,] 1227 0.0104240672
## [449,] 1228 0.0096775099
## [450,] 1229 0.0113270197
## [451,] 1230 0.0182960967
## [452,] 1231 0.0220185922
## [453,] 1232 0.0236599456
## [454,] 1233 0.0254369951
## [455,] 1234 0.0276801937
## [456,] 1235 0.0316370919
## [457,] 1236 0.0353802361
## [458,] 1237 0.0389178162
## [459,] 1238 0.0425586196
## [460,] 1239 0.0461384606
## [461,] 1240 0.0490390790
## [462,] 1241 0.0523822397
## [463,] 1242 0.0563516410
## [464,] 1243 0.0608608120
## [465,] 1244 0.0656339697
## [466,] 1245 0.0731271910
## [467,] 1246 0.0796402441
## [468,] 1247 0.0840734231
## [469,] 1248 0.0901857243
## [470,] 1249 0.0973139201
## [471,] 1250 0.0997638670
## [472,] 1251 0.1024650794
## [473,] 1252 0.1072575480
## [474,] 1253 0.1110564579
## [475,] 1254 0.1139855485
## [476,] 1255 0.1190721451
## [477,] 1256 0.1240820227
## [478,] 1257 0.1261203034
## [479,] 1258 0.1292359506
## [480,] 1259 0.1336725414
## [481,] 1260 0.1403035453
## [482,] 1261 0.1474069666
## [483,] 1262 0.1507260955
## [484,] 1263 0.1557297605
## [485,] 1264 0.1636610999
## [486,] 1265 0.1705073158
## [487,] 1266 0.1768235875
## [488,] 1267 0.1902811291
## [489,] 1268 0.2046218779
## [490,] 1269 0.2208024107
## [491,] 1270 0.2426028147
## [492,] 1271 0.2682173628
## [493,] 1272 0.3058444176
## [494,] 1273 0.3482662139
## [495,] 1274 0.4037607802
## [496,] 1275 0.4614470288
## [497,] 1276 0.5211443748
## [498,] 1277 0.5891991548
## [499,] 1278 0.6575407460
## [500,] 1279 0.7170374633
## [501,] 1280 0.7765464495
## [502,] 1281 0.8360704784
## [503,] 1282 0.8867307256
## [504,] 1283 0.9336560496
## [505,] 1284 0.9682549658
## [506,] 1285 0.9920065112
## [507,] 1286 0.9981240973
## [508,] 1287 0.9913978887
## [509,] 1288 0.9789057531
## [510,] 1289 0.9445578049
## [511,] 1290 0.9055721485
## [512,] 1291 0.8565667408
## [513,] 1292 0.7971161822
## [514,] 1293 0.7315023291
## [515,] 1294 0.6617238830
## [516,] 1295 0.5934631223
## [517,] 1296 0.5303436476
## [518,] 1297 0.4701425320
## [519,] 1298 0.4121401847
## [520,] 1299 0.3617733101
## [521,] 1300 0.3142460179
## [522,] 1301 0.2741493408
## [523,] 1302 0.2412469205
## [524,] 1303 0.2151557895
## [525,] 1304 0.1958384840
## [526,] 1305 0.1790275184
## [527,] 1306 0.1688343440
## [528,] 1307 0.1578029654
## [529,] 1308 0.1457835743
## [530,] 1309 0.1409083521
## [531,] 1310 0.1378654254
## [532,] 1311 0.1324760376
## [533,] 1312 0.1304795932
## [534,] 1313 0.1334021075
## [535,] 1314 0.1361051675
## [536,] 1315 0.1386527835
## [537,] 1316 0.1396514790
## [538,] 1317 0.1399499918
## [539,] 1318 0.1390213015
## [540,] 1319 0.1394081736
## [541,] 1320 0.1404094764
## [542,] 1321 0.1430119238
## [543,] 1322 0.1451141726
## [544,] 1323 0.1461454420
## [545,] 1324 0.1446925299
## [546,] 1325 0.1418341949
## [547,] 1326 0.1401221531
## [548,] 1327 0.1381181277
## [549,] 1328 0.1352341519
## [550,] 1329 0.1345778622
## [551,] 1330 0.1354091177
## [552,] 1331 0.1375319297
## [553,] 1332 0.1393353760
## [554,] 1333 0.1397877208
## [555,] 1334 0.1409294064
## [556,] 1335 0.1426072457
## [557,] 1336 0.1436096979
## [558,] 1337 0.1442286427
## [559,] 1338 0.1437155987
## [560,] 1339 0.1472691077
## [561,] 1340 0.1544107517
## [562,] 1341 0.1629802388
## [563,] 1342 0.1705330801
## [564,] 1343 0.1722488189
## [565,] 1344 0.1796485471
## [566,] 1345 0.1926880344
## [567,] 1346 0.1935467921
## [568,] 1347 0.1928771211
## [569,] 1348 0.1980512204
## [570,] 1349 0.2017835835
## [571,] 1350 0.2039199483
## [572,] 1351 0.2029814963
## [573,] 1352 0.2018016690
## [574,] 1353 0.2042224678
## [575,] 1354 0.2094303633
## [576,] 1355 0.2180019964
## [577,] 1356 0.2227028470
## [578,] 1357 0.2272906275
## [579,] 1358 0.2402170876
## [580,] 1359 0.2560411069
## [581,] 1360 0.2756502219
## [582,] 1361 0.2895296628
## [583,] 1362 0.3018732770
## [584,] 1363 0.3170797563
## [585,] 1364 0.3324678483
## [586,] 1365 0.3481108800
## [587,] 1366 0.3588774127
## [588,] 1367 0.3676705045
## [589,] 1368 0.3725481020
## [590,] 1369 0.3775076319
## [591,] 1370 0.3825878831
## [592,] 1371 0.3894465057
## [593,] 1372 0.3965480946
## [594,] 1373 0.3975487096
## [595,] 1374 0.3985289654
## [596,] 1375 0.3994779770
## [597,] 1376 0.4000545284
## [598,] 1377 0.4000806020
## [599,] 1378 0.3930380860
## [600,] 1379 0.3880195849
## [601,] 1380 0.3862108818
## [602,] 1381 0.3764096771
## [603,] 1382 0.3636701812
## [604,] 1383 0.3544674650
## [605,] 1384 0.3434402353
## [606,] 1385 0.3294956409
## [607,] 1386 0.3156465350
## [608,] 1387 0.3015211135
## [609,] 1388 0.2815254786
## [610,] 1389 0.2643523470
## [611,] 1390 0.2516923482
## [612,] 1391 0.2353294326
## [613,] 1392 0.2179550754
## [614,] 1393 0.2084400387
## [615,] 1394 0.1977170989
## [616,] 1395 0.1850785976
## [617,] 1396 0.1771976697
## [618,] 1397 0.1711112732
## [619,] 1398 0.1645027359
## [620,] 1399 0.1600501123
## [621,] 1400 0.1589332578
## [622,] 1401 0.1564915704
## [623,] 1402 0.1535747565
## [624,] 1403 0.1508507609
## [625,] 1404 0.1491909302
## [626,] 1405 0.1491118477
## [627,] 1406 0.1488214776
## [628,] 1407 0.1481890537
## [629,] 1408 0.1443953159
## [630,] 1409 0.1424280189
## [631,] 1410 0.1430245951
## [632,] 1411 0.1421703961
## [633,] 1412 0.1408573669
## [634,] 1413 0.1396868208
## [635,] 1414 0.1388976589
## [636,] 1415 0.1386106808
## [637,] 1416 0.1391474280
## [638,] 1417 0.1405110326
## [639,] 1418 0.1465273577
## [640,] 1419 0.1489837340
## [641,] 1420 0.1471096119
## [642,] 1421 0.1479983687
## [643,] 1422 0.1495409496
## [644,] 1423 0.1505865094
## [645,] 1424 0.1518668552
## [646,] 1425 0.1534071086
## [647,] 1426 0.1558449593
## [648,] 1427 0.1580904801
## [649,] 1428 0.1582245320
## [650,] 1429 0.1588156760
## [651,] 1430 0.1598639121
## [652,] 1431 0.1588003878
## [653,] 1432 0.1574175347
## [654,] 1433 0.1563750621
## [655,] 1434 0.1529090946
## [656,] 1435 0.1472879167
## [657,] 1436 0.1423957487
## [658,] 1437 0.1379552897
## [659,] 1438 0.1347637139
## [660,] 1439 0.1301735494
## [661,] 1440 0.1244869309
## [662,] 1441 0.1185669125
## [663,] 1442 0.1134528821
## [664,] 1443 0.1108292496
## [665,] 1444 0.1084861752
## [666,] 1445 0.1063335123
## [667,] 1446 0.1057110706
## [668,] 1447 0.1064582374
## [669,] 1448 0.1103696525
## [670,] 1449 0.1118303451
## [671,] 1450 0.1118809319
## [672,] 1451 0.1132456610
## [673,] 1452 0.1152404986
## [674,] 1453 0.1184719564
## [675,] 1454 0.1239252152
## [676,] 1455 0.1303181729
## [677,] 1456 0.1314504733
## [678,] 1457 0.1315717381
## [679,] 1458 0.1299938286
## [680,] 1459 0.1289645037
## [681,] 1460 0.1279680558
## [682,] 1461 0.1245953183
## [683,] 1462 0.1184223633
## [684,] 1463 0.1083812026
## [685,] 1464 0.1004662246
## [686,] 1465 0.0933155268
## [687,] 1466 0.0871075177
## [688,] 1467 0.0800079943
## [689,] 1468 0.0719059792
## [690,] 1469 0.0693380632
## [691,] 1470 0.0669654344
## [692,] 1471 0.0596929639
## [693,] 1472 0.0540365195
## [694,] 1473 0.0498513853
## [695,] 1474 0.0468435153
## [696,] 1475 0.0440626688
## [697,] 1476 0.0414827002
## [698,] 1477 0.0402681996
## [699,] 1478 0.0400421694
## [700,] 1479 0.0392729721
## [701,] 1480 0.0388874590
## [702,] 1481 0.0395606385
## [703,] 1482 0.0388796513
## [704,] 1483 0.0374325457
## [705,] 1484 0.0386193781
## [706,] 1485 0.0406983069
## [707,] 1486 0.0444220433
## [708,] 1487 0.0424049178
## [709,] 1488 0.0383106389
## [710,] 1489 0.0399917707
## [711,] 1490 0.0416074057
## [712,] 1491 0.0431296067
## [713,] 1492 0.0439189238
## [714,] 1493 0.0444032356
## [715,] 1494 0.0443059273
## [716,] 1495 0.0448726590
## [717,] 1496 0.0461574169
## [718,] 1497 0.0454143807
## [719,] 1498 0.0442268761
## [720,] 1499 0.0428245307
## [721,] 1500 0.0424951588
## [722,] 1501 0.0430340249
## [723,] 1502 0.0395760449
## [724,] 1503 0.0372976673
## [725,] 1504 0.0393280755
## [726,] 1505 0.0398044694
## [727,] 1506 0.0393562001
## [728,] 1507 0.0396172300
## [729,] 1508 0.0396390191
## [730,] 1509 0.0391893991
## [731,] 1510 0.0407139011
## [732,] 1511 0.0426480503
## [733,] 1512 0.0385425186
## [734,] 1513 0.0353963676
## [735,] 1514 0.0335544444
## [736,] 1515 0.0323618162
## [737,] 1516 0.0315463726
## [738,] 1517 0.0318934501
## [739,] 1518 0.0308012772
## [740,] 1519 0.0283141359
## [741,] 1520 0.0260811472
## [742,] 1521 0.0243297722
## [743,] 1522 0.0240587278
## [744,] 1523 0.0219107922
## [745,] 1524 0.0184786679
## [746,] 1525 0.0175825023
## [747,] 1526 0.0169371874
## [748,] 1527 0.0165825603
## [749,] 1528 0.0178944346
## [750,] 1529 0.0198407099
## [751,] 1530 0.0190023342
## [752,] 1531 0.0168859766
## [753,] 1532 0.0129317575
## [754,] 1533 0.0116403732
## [755,] 1534 0.0113732119
## [756,] 1535 0.0126513949
## [757,] 1536 0.0117826606
## [758,] 1537 0.0088167414
## [759,] 1538 0.0104359440
## [760,] 1539 0.0118060780
## [761,] 1540 0.0101788145
## [762,] 1541 0.0085842363
## [763,] 1542 0.0070111309
## [764,] 1543 0.0084854392
## [765,] 1544 0.0093164146
## [766,] 1545 0.0086813610
## [767,] 1546 0.0087463642
## [768,] 1547 0.0091691425
## [769,] 1548 0.0103903728
## [770,] 1549 0.0110905831
## [771,] 1550 0.0111262034
## [772,] 1551 0.0105083903
## [773,] 1552 0.0102933901
## [774,] 1553 0.0127015769
## [775,] 1554 0.0149774713
## [776,] 1555 0.0171428849
## [777,] 1556 0.0186684541
## [778,] 1557 0.0186858710
## [779,] 1558 0.0150733767
## [780,] 1559 0.0119871565
## [781,] 1560 0.0092681853
## [782,] 1561 0.0099726072
## [783,] 1562 0.0109439084
## [784,] 1563 0.0123247771
## [785,] 1564 0.0122757318
## [786,] 1565 0.0115712203
## [787,] 1566 0.0095142858
## [788,] 1567 0.0097482992
## [789,] 1568 0.0121854814
## [790,] 1569 0.0126125161
## [791,] 1570 0.0125729309
## [792,] 1571 0.0119462850
## [793,] 1572 0.0106388281
## [794,] 1573 0.0089262779
## [795,] 1574 0.0135313249
## [796,] 1575 0.0160617654
## [797,] 1576 0.0151055651
## [798,] 1577 0.0128501051
## [799,] 1578 0.0106301926
## [800,] 1579 0.0118444748
## [801,] 1580 0.0133146721
## [802,] 1581 0.0150448143
## [803,] 1582 0.0140989330
## [804,] 1583 0.0142903660
## [805,] 1584 0.0191790965
## [806,] 1585 0.0214554408
## [807,] 1586 0.0221512513
## [808,] 1587 0.0204009904
## [809,] 1588 0.0188495510
## [810,] 1589 0.0176374584
## [811,] 1590 0.0180631323
## [812,] 1591 0.0190219431
## [813,] 1592 0.0199326825
## [814,] 1593 0.0208982230
## [815,] 1594 0.0219172951
## [816,] 1595 0.0232260826
## [817,] 1596 0.0244157433
## [818,] 1597 0.0251608742
## [819,] 1598 0.0257840553
## [820,] 1599 0.0263723150
## [821,] 1600 0.0288648052
## [822,] 1601 0.0298402890
## [823,] 1602 0.0285066316
## [824,] 1603 0.0289995125
## [825,] 1604 0.0299814685
## [826,] 1605 0.0307792656
## [827,] 1606 0.0316347920
## [828,] 1607 0.0325400704
## [829,] 1608 0.0301859372
## [830,] 1609 0.0283207676
## [831,] 1610 0.0283214388
## [832,] 1611 0.0295107192
## [833,] 1612 0.0312998600
## [834,] 1613 0.0336269540
## [835,] 1614 0.0376140991
## [836,] 1615 0.0437020191
## [837,] 1616 0.0423284911
## [838,] 1617 0.0409369401
## [839,] 1618 0.0455713514
## [840,] 1619 0.0492186361
## [841,] 1620 0.0521795091
## [842,] 1621 0.0562887600
## [843,] 1622 0.0607059647
## [844,] 1623 0.0656230096
## [845,] 1624 0.0701540077
## [846,] 1625 0.0748614385
## [847,] 1626 0.0817522088
## [848,] 1627 0.0872677090
## [849,] 1628 0.0914502523
## [850,] 1629 0.0972779357
## [851,] 1630 0.1034229717
## [852,] 1631 0.1098863891
## [853,] 1632 0.1166672893
## [854,] 1633 0.1238524767
## [855,] 1634 0.1355925874
## [856,] 1635 0.1449096161
## [857,] 1636 0.1511112536
## [858,] 1637 0.1549272993
## [859,] 1638 0.1589852489
## [860,] 1639 0.1658513001
## [861,] 1640 0.1734439787
## [862,] 1641 0.1815258254
## [863,] 1642 0.1854854617
## [864,] 1643 0.1879674190
## [865,] 1644 0.1880759152
## [866,] 1645 0.1907102602
## [867,] 1646 0.1940266784
## [868,] 1647 0.1967326129
## [869,] 1648 0.1989168181
## [870,] 1649 0.2006549060
## [871,] 1650 0.2034223439
## [872,] 1651 0.2056262541
## [873,] 1652 0.2064519141
## [874,] 1653 0.2077877247
## [875,] 1654 0.2088967642
## [876,] 1655 0.2060631327
## [877,] 1656 0.2070057954
## [878,] 1657 0.2118752079
## [879,] 1658 0.2104939260
## [880,] 1659 0.2090573401
## [881,] 1660 0.2097410530
## [882,] 1661 0.2058879440
## [883,] 1662 0.2001280968
## [884,] 1663 0.2008364895
## [885,] 1664 0.2007708023
## [886,] 1665 0.1997560265
## [887,] 1666 0.1978511284
## [888,] 1667 0.1957459144
## [889,] 1668 0.1935327991
## [890,] 1669 0.1895544345
## [891,] 1670 0.1845627300
## [892,] 1671 0.1812567587
## [893,] 1672 0.1760848106
## [894,] 1673 0.1682934906
## [895,] 1674 0.1661871888
## [896,] 1675 0.1641983171
## [897,] 1676 0.1575139943
## [898,] 1677 0.1493171636
## [899,] 1678 0.1401103504
## [900,] 1679 0.1375688174
## [901,] 1680 0.1327667591
## [902,] 1681 0.1244266297
## [903,] 1682 0.1172614506
## [904,] 1683 0.1106633136
## [905,] 1684 0.1051735456
## [906,] 1685 0.0976214477
## [907,] 1686 0.0885883463
## [908,] 1687 0.0832067510
## [909,] 1688 0.0781697632
## [910,] 1689 0.0735698486
## [911,] 1690 0.0669943463
## [912,] 1691 0.0605993712
## [913,] 1692 0.0578625350
## [914,] 1693 0.0544674195
## [915,] 1694 0.0505763391
## [916,] 1695 0.0469317908
## [917,] 1696 0.0446375686
## [918,] 1697 0.0446383269
## [919,] 1698 0.0419442243
## [920,] 1699 0.0384607861
## [921,] 1700 0.0350653012
## [922,] 1701 0.0326251763
## [923,] 1702 0.0309106009
## [924,] 1703 0.0326762206
## [925,] 1704 0.0343761321
## [926,] 1705 0.0357744272
## [927,] 1706 0.0330542199
## [928,] 1707 0.0301838330
## [929,] 1708 0.0323976907
## [930,] 1709 0.0327971728
## [931,] 1710 0.0318620132
## [932,] 1711 0.0326319127
## [933,] 1712 0.0331176894
## [934,] 1713 0.0330807267
## [935,] 1714 0.0341905464
## [936,] 1715 0.0349732598
## [937,] 1716 0.0329629447
## [938,] 1717 0.0358504193
## [939,] 1718 0.0421162355
## [940,] 1719 0.0409096971
## [941,] 1720 0.0408165882
## [942,] 1721 0.0424710327
## [943,] 1722 0.0436245132
## [944,] 1723 0.0446979778
## [945,] 1724 0.0459288165
## [946,] 1725 0.0467420074
## [947,] 1726 0.0474010626
## [948,] 1727 0.0534346129
## [949,] 1728 0.0577123378
## [950,] 1729 0.0596221123
## [951,] 1730 0.0602862705
## [952,] 1731 0.0616155925
## [953,] 1732 0.0658438459
## [954,] 1733 0.0685572697
## [955,] 1734 0.0705430187
## [956,] 1735 0.0742690683
## [957,] 1736 0.0757143609
## [958,] 1737 0.0745132094
## [959,] 1738 0.0788637984
## [960,] 1739 0.0824457001
## [961,] 1740 0.0819241089
## [962,] 1741 0.0818310009
## [963,] 1742 0.0819741210
## [964,] 1743 0.0825003616
## [965,] 1744 0.0802122520
## [966,] 1745 0.0751749856
## [967,] 1746 0.0747653156
## [968,] 1747 0.0732698489
## [969,] 1748 0.0686547718
## [970,] 1749 0.0724111392
## [971,] 1750 0.0772495915
#test savitzky-golay smoother - works like a charm nice and easy
plastic_approx_smoother <- sgolayfilt(plastic_approx, 3,11)
plot(hyper_baseline_corr@wavelength, hyper_baseline_corr@data[["spc"]], type = "l")+
lines(freq_index, plastic_approx, col = "red")+lines(freq_index, plastic_approx_smoother, col = "green")
## integer(0)
#Black means its approx poorly - red is good approximation
unique(ref_lib$poly_lab)
## [1] "ABS" "HDPE" "LDPE" "PA" "PC" "PET" "PLA" "PMMA" "Poly" "POM"
## [11] "PP" "PS" "PSA" "PVC" "."
abs_reference <- dplyr::filter(plastic_ref_lib, SAMPLE == "ABS")
cor.test(plastic_approx, abs_reference$INTENSITY, method = "pearson")
##
## Pearson's product-moment correlation
##
## data: plastic_approx and abs_reference$INTENSITY
## t = -3.2904, df = 969, p-value = 0.001036
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.16692691 -0.04248696
## sample estimates:
## cor
## -0.1051184
HDPE_reference <- dplyr::filter(plastic_ref_lib, SAMPLE == "HDPE")
cor.test(plastic_approx, HDPE_reference$INTENSITY)
##
## Pearson's product-moment correlation
##
## data: plastic_approx and HDPE_reference$INTENSITY
## t = 2.6683, df = 969, p-value = 0.007751
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.0226140 0.1475247
## sample estimates:
## cor
## 0.08540492
LDPE_reference <- dplyr::filter(plastic_ref_lib, SAMPLE == "LDPE")
cor.test(plastic_approx, LDPE_reference$INTENSITY)
##
## Pearson's product-moment correlation
##
## data: plastic_approx and LDPE_reference$INTENSITY
## t = 1.5896, df = 969, p-value = 0.1122
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.01195106 0.11354782
## sample estimates:
## cor
## 0.05099971
plot(freq_index, LDPE_reference$INTENSITY, type = "l")+
lines(freq_index, plastic_approx, col = "red")
## integer(0)
PA_reference <- dplyr::filter(plastic_ref_lib, SAMPLE == "PA")
cor.test(plastic_approx, PA_reference$INTENSITY, method = "pearson")
##
## Pearson's product-moment correlation
##
## data: plastic_approx and PA_reference$INTENSITY
## t = -2.0275, df = 969, p-value = 0.04289
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.127384974 -0.002089523
## sample estimates:
## cor
## -0.06499341
plot(freq_index, PA_reference$INTENSITY, type = "l")+
lines(freq_index, plastic_approx, col = "red")
## integer(0)
PC_reference <- dplyr::filter(plastic_ref_lib, SAMPLE == "PC")
cor.test(plastic_approx, PC_reference$INTENSITY, method = "pearson")
##
## Pearson's product-moment correlation
##
## data: plastic_approx and PC_reference$INTENSITY
## t = -4.4571, df = 969, p-value = 9.277e-06
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.20284087 -0.07953396
## sample estimates:
## cor
## -0.1417372
plot(freq_index, PC_reference$INTENSITY, type = "l")+
lines(freq_index, plastic_approx, col = "red")
## integer(0)
PET_reference <- dplyr::filter(plastic_ref_lib, SAMPLE == "PET")
cor.test(plastic_approx, PET_reference$INTENSITY, method = "pearson")
##
## Pearson's product-moment correlation
##
## data: plastic_approx and PET_reference$INTENSITY
## t = -3.6079, df = 969, p-value = 0.0003244
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.17676533 -0.05260185
## sample estimates:
## cor
## -0.1151333
PLA_reference <- dplyr::filter(plastic_ref_lib, SAMPLE == "PLA")
cor.test(plastic_approx, PLA_reference$INTENSITY, method = "pearson")
##
## Pearson's product-moment correlation
##
## data: plastic_approx and PLA_reference$INTENSITY
## t = 1.8277, df = 969, p-value = 0.0679
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.004315159 0.121079132
## sample estimates:
## cor
## 0.05861318
PMMA_reference <- dplyr::filter(plastic_ref_lib, SAMPLE == "PMMA")
cor.test(plastic_approx, PMMA_reference$INTENSITY, method = "pearson")
##
## Pearson's product-moment correlation
##
## data: plastic_approx and PMMA_reference$INTENSITY
## t = -3.2815, df = 969, p-value = 0.001069
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.16664984 -0.04220247
## sample estimates:
## cor
## -0.1048366
Poly_reference <- dplyr::filter(plastic_ref_lib, SAMPLE == "Poly")
cor.test(plastic_approx, Poly_reference$INTENSITY, method = "pearson")
##
## Pearson's product-moment correlation
##
## data: plastic_approx and Poly_reference$INTENSITY
## t = -5.7582, df = 969, p-value = 1.141e-08
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.2420367 -0.1203589
## sample estimates:
## cor
## -0.181894
POM_reference <- dplyr::filter(plastic_ref_lib, SAMPLE == "POM")
cor.test(plastic_approx, POM_reference$INTENSITY, method = "pearson")
##
## Pearson's product-moment correlation
##
## data: plastic_approx and POM_reference$INTENSITY
## t = 4.6299, df = 969, p-value = 4.157e-06
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.08498897 0.20810118
## sample estimates:
## cor
## 0.1471148
PP_reference <- dplyr::filter(plastic_ref_lib, SAMPLE == "PP")
cor.test(plastic_approx, PP_reference$INTENSITY, method = "pearson")
##
## Pearson's product-moment correlation
##
## data: plastic_approx and PP_reference$INTENSITY
## t = 4.0798, df = 969, p-value = 4.879e-05
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.0675895 0.1912978
## sample estimates:
## cor
## 0.1299494
PS_reference <- dplyr::filter(plastic_ref_lib, SAMPLE == "PS")
cor.test(plastic_approx, PS_reference$INTENSITY, method = "pearson")
##
## Pearson's product-moment correlation
##
## data: plastic_approx and PS_reference$INTENSITY
## t = -6.3449, df = 969, p-value = 3.41e-10
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.2593757 -0.1385508
## sample estimates:
## cor
## -0.1997223
PSA_reference <- dplyr::filter(plastic_ref_lib, SAMPLE == "PSA")
cor.test(plastic_approx, PSA_reference$INTENSITY, method = "pearson")
##
## Pearson's product-moment correlation
##
## data: plastic_approx and PSA_reference$INTENSITY
## t = -4.6472, df = 969, p-value = 3.829e-06
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.20862802 -0.08553572
## sample estimates:
## cor
## -0.1476536
PVC_reference <- dplyr::filter(plastic_ref_lib, SAMPLE == "PVC")
cor.test(plastic_approx, PVC_reference$INTENSITY, method = "pearson")
##
## Pearson's product-moment correlation
##
## data: plastic_approx and PVC_reference$INTENSITY
## t = 10.367, df = 969, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.2582033 0.3715099
## sample estimates:
## cor
## 0.3159829
cor(plastic_approx, PVC_reference$INTENSITY, method = "pearson")
## [1] 0.3159829
plot(freq_index, PVC_reference$INTENSITY, type = "l")+
lines(freq_index, plastic_approx, col = "red")
## integer(0)
#Same value as cor.test
#This is what they used for their hit index in Choy et al. 2019
#PVC is the highest correlation by far
#This version is deprecated as it takes all the spectra from that study and not just the plastic reference.
#poly_labs <- as.character(unique(ref_lib$poly_lab))
poly_labs <-as.character(unique(plastic_ref_lib$poly_lab))
cor_table <- as.data.frame(matrix(ncol = length(poly_labs), nrow = 1, dimnames = list(NULL, poly_labs)))
#for(name in poly_labs){
# temp_filter <- filter(plastic_ref_lib, poly_lab == name)
# cor_table[,name] <- cor(plastic_approx, temp_filter$INTENSITY, method = "pearson")
#}
#That for loop did not work
plastic_lab <- data.frame()
for(i in 1:length(poly_labs)){
temp_filter <- dplyr::filter(plastic_ref_lib, poly_lab == poly_labs[i])
cor_table[,i] <- round(cor(plastic_approx, temp_filter$INTENSITY, method = "pearson"), digits = 3)
for (d in 1:nrow(cor_table)){
plastic_lab[d,1]<-colnames(cor_table[which.max(cor_table[d,])])
}
}
ggtexttable(cor_table, theme = ttheme(base_size = 7.5)) %>% table_cell_bg(row = 2, column = 15, fill = "red")
setwd("/Users/christophercarnivale/Desktop/Dissertation_data/Raman Spectral Data copy/10_24_19_spectral_data/")
plastic_test_files <- list.files(path = "/Users/christophercarnivale/Desktop/Dissertation_data/Raman Spectral Data copy/10_24_19_spectral_data/", pattern = "*.txt")
file_numbers <- seq(plastic_test_files)
plastic_test_spc_table <- matrix()
freq_index <- seq(780, 1750, by = 1)
all_sample_particle_WV <- as.data.frame(freq_index)
#start of a larger raman data import function need to uncomment for it to work
#Raman_large_import <- function(filepath){
#freq_index <- seq(780, 1750, by = 1)
#all_sample_particle_WV <- as.data.frame(freq_index)
#for loop over .txt files and create a df
for(filename in plastic_test_files){
temp_csv <- read.delim(file = filename, header = F, sep = "\t")
colnames(temp_csv) <- c("Wavelength", "Counts")
temp_csv_matrix <- as.matrix(temp_csv)
approx_test_fun <- approxfun(x = temp_csv_matrix[,'Wavelength'], y = temp_csv_matrix[,'Counts'])
new_counts <- approx_test_fun(freq_index)
all_sample_particle_WV[filename] <- new_counts
}
all_sample_particle_WV_t <- t(all_sample_particle_WV)
colnames(all_sample_particle_WV_t) <- all_sample_particle_WV_t[1,]
all_sample_particle_WV_t <- all_sample_particle_WV_t[-1,]
potplastic_filename <- rownames(all_sample_particle_WV_t)
potp_hyper <- as.hyperSpec(all_sample_particle_WV_t, data = data.frame(potplastic_filename))
#This version of as.hyper works and keeps the filenames for each raman spectra
#Baselines
potp_baselines <- spc.fit.poly.below(potp_hyper, poly.order = 6)
#Plotting baseline corrections
plot(potp_hyper-potp_baselines)
title(xlab = "Wavelength",ylab = "Arbitrary unit of excitation")
potp_hyper_corrected <- potp_hyper-potp_baselines
#Standard Normal variate normalization
potp_hyper_corrected@data[["spc"]] <- standardNormalVariate(potp_hyper_corrected@data[["spc"]])
#min-max scaling
potp_hyper_corrected@data[["spc"]] <- (potp_hyper_corrected@data[["spc"]] - min(potp_hyper_corrected@data[["spc"]])) / (max(potp_hyper_corrected@data[["spc"]])-min(potp_hyper_corrected@data[["spc"]]))
#}
plot(potp_hyper_corrected, col = rainbow(39),title.args = list(xlab = "Wavelength",ylab = "Arbitrary unit of Intensity"))
#View(t(all_sample_particle_WV))
This chunk now imports and loops over all .txt files which will correspond to individual particles and transform them into a matrix for proper raman processing. Now I need to make them into a hyperspec obj and do the proper standard normal variate, baseline correction, and min-max standardization.
#poly_labs <- as.character(unique(ref_lib$poly_lab))
#poly_labs <- poly_labs[-15]
#poly_labs <-as.character(unique(plastic_ref_lib$poly_lab))
poly_labs <-as.character(unique(plastic_ref_lib$poly_lab))
cor_table_manyplastic <- as.data.frame(matrix(ncol = length(poly_labs), nrow = nrow(potp_hyper_corrected@data[["spc"]]), dimnames = list(NULL, poly_labs)))
for(row in 1:nrow(potp_hyper_corrected@data[["spc"]])){
for(i in seq_along(poly_labs)){
temp_filter <- dplyr::filter(plastic_ref_lib, poly_lab == poly_labs[i])
cor_table_manyplastic[row,i] <- round(cor(potp_hyper_corrected@data[["spc"]][row,], temp_filter$INTENSITY, method = "pearson"), digits = 3)
}
}
plastic_lab <- as.data.frame(matrix(nrow = nrow(cor_table_manyplastic), ncol = 2))
colnames(plastic_lab) <- c("Hit_Index_Value","Polymer_ID")
rownames(plastic_lab) <- plastic_test_files
for (d in 1:nrow(cor_table_manyplastic)){
plastic_lab[d,1]<-cor_table_manyplastic[d,which.max(cor_table_manyplastic[d,])]
plastic_lab[d,2]<-colnames(cor_table_manyplastic[which.max(cor_table_manyplastic[d,])])
}
plastic_lab_filt <- plastic_lab %>%
rownames_to_column('filename') %>%
dplyr::filter(Hit_Index_Value > 0.22) %>%
column_to_rownames('filename')
plastic_lab_count <- group_by(plastic_lab_filt, Polymer_ID) %>% summarise(n())
plastic_lab_count
## # A tibble: 4 x 2
## Polymer_ID `n()`
## <chr> <int>
## 1 PA 13
## 2 PET 1
## 3 PMMA 1
## 4 POM 3
ggtexttable(plastic_lab_count)
ggtexttable(plastic_lab_filt)
#temp_row <- potp_hyper_corrected@data[["spc"]][row,]
#for(row in 1:nrow(potp_hyper_corrected@data[["spc"]])){
#print(potp_hyper_corrected@data[["spc"]][row,])
#}
#for(i in 1:length(poly_labs)){
# temp_filter <- filter(plastic_ref_lib, poly_lab == poly_labs[i])
# return(temp_filter)
#}
#Does this pipeline hold up to with the new spectral data?
#If so, I need to add in additional reference from openspecy, data which I can download
#Making notes of each step to make it more readable for future reference
setwd("/Users/christophercarnivale/Desktop/Dissertation_data/Raman Spectral Data copy/Antarctic_Microplastic_Raman_Spectra/6_13_23_Raman_test_runs/")
#Creates a list of all file names within the desired folder
#Each folder with house an entire samples spectral data
plastic_test_files <- list.files(path = "/Users/christophercarnivale/Desktop/Dissertation_data/Raman Spectral Data copy/Antarctic_Microplastic_Raman_Spectra/6_13_23_Raman_test_runs/", pattern = "*.txt")
#Index values to be looped over
file_numbers <- seq(plastic_test_files)
#Blank matrix table to be inserted into an hyperspec object
plastic_test_spc_table <- matrix()
#Desired range of spectral data for identification
#data range collected is much larger, ranging form ~100 - ~2500 wavelength
freq_index <- seq(780, 1750, by = 1)
#Empty dataframe from which to add in each spectra
all_sample_particle_WV <- as.data.frame(freq_index)
#start of a larger raman data import function need to uncomment for it to work
#Raman_large_import <- function(filepath){
#freq_index <- seq(780, 1750, by = 1)
#all_sample_particle_WV <- as.data.frame(freq_index)
#for loop over .txt files and create a df of spectra
#This loops and adds the smoothed version of the spectra within the wavelength ranges desired, which can be changed at any time by changing the "freq_index" object
for(filename in plastic_test_files){
temp_csv <- read.delim(file = filename, header = F, sep = "\t")
colnames(temp_csv) <- c("Wavelength", "Counts")
temp_csv_matrix <- as.matrix(temp_csv)
approx_test_fun <- approxfun(x = temp_csv_matrix[,'Wavelength'], y = temp_csv_matrix[,'Counts'])
new_counts <- approx_test_fun(freq_index)
all_sample_particle_WV[filename] <- new_counts
}
#Matrix is in the wrong format and needs to by transposed to fit the hyperspec obj
all_sample_particle_WV_t <- t(all_sample_particle_WV)
#Converting the first column into column names
colnames(all_sample_particle_WV_t) <- all_sample_particle_WV_t[1,]
all_sample_particle_WV_t <- all_sample_particle_WV_t[-1,]
#Extracting the filenames or name of specific spectra being analyzed
potplastic_filename <- rownames(all_sample_particle_WV_t)
#Making hyperspec obj with matrix of spectra with filename meta data
potp_hyper <- as.hyperSpec(all_sample_particle_WV_t, data = data.frame(potplastic_filename))
#This version of as.hyper works and keeps the filenames for each raman spectra
#Baseline correction
potp_baselines <- spc.fit.poly.below(potp_hyper, poly.order = 6)
#Plotting baseline corrections
plot(potp_hyper-potp_baselines)
title(xlab = "Wavelength",ylab = "Arbitrary unit of excitation")
potp_hyper_corrected <- potp_hyper-potp_baselines
#Standard Normal variate normalization
potp_hyper_corrected@data[["spc"]] <- standardNormalVariate(potp_hyper_corrected@data[["spc"]])
#min-max scaling
potp_hyper_corrected@data[["spc"]] <- (potp_hyper_corrected@data[["spc"]] - min(potp_hyper_corrected@data[["spc"]])) / (max(potp_hyper_corrected@data[["spc"]])-min(potp_hyper_corrected@data[["spc"]]))
#}
plot(potp_hyper_corrected, col = rainbow(39),title.args = list(xlab = "Wavelength",ylab = "Arbitrary unit of Intensity"))
#Extracting unique names from reference library
#poly_labs <- as.character(unique(ref_lib$poly_lab))
#remove the last column (why?)
#poly_labs <- poly_labs[-15]
#This previous version took all samples from the study and not just the reference plastic. All of the categorized "." were environmental samples thus necessitating removal. I had a filtered df for plastic reference only so use that df for plastic label names.
poly_labs <-as.character(unique(plastic_ref_lib$poly_lab))
#Create an correlation matrix to fill that are the dimensions of the plastic reference vs the number of spectra collected within a single sample
cor_table_manyplastic <- as.data.frame(matrix(ncol = length(poly_labs), nrow = nrow(potp_hyper_corrected@data[["spc"]]), dimnames = list(NULL, poly_labs)))
#Calculating correlations and filling the DF with the values
for(row in 1:nrow(potp_hyper_corrected@data[["spc"]])){
for(i in seq_along(poly_labs)){
temp_filter <- dplyr::filter(plastic_ref_lib, poly_lab == poly_labs[i])
cor_table_manyplastic[row,i] <- round(cor(potp_hyper_corrected@data[["spc"]][row,], temp_filter$INTENSITY, method = "pearson"), digits = 3)
}
}
#Creating an empty df to hold the identified plastic info in
plastic_lab <- as.data.frame(matrix(nrow = nrow(cor_table_manyplastic), ncol = 2))
colnames(plastic_lab) <- c("Hit_Index_Value","Polymer_ID")
rownames(plastic_lab) <- plastic_test_files
#Search the correlation matrix for plastic that are the highest correlated to the spectra with the filename (or really particle name).
for (d in 1:nrow(cor_table_manyplastic)){
plastic_lab[d,1]<-cor_table_manyplastic[d,which.max(cor_table_manyplastic[d,])]
plastic_lab[d,2]<-colnames(cor_table_manyplastic[which.max(cor_table_manyplastic[d,])])
}
#Filter spectra that don't meet the spectra limit
plastic_lab_filt <- plastic_lab %>%
rownames_to_column('filename') %>%
dplyr::filter(Hit_Index_Value > 0.22) %>%
column_to_rownames('filename')
#Summarise the counts for each polymer - But I need to have a way to add to a df with polymers unidentified in the sample but we have references for.
plastic_lab_count <- group_by(plastic_lab_filt, Polymer_ID) %>% summarise(n())
plastic_lab_count
## # A tibble: 6 x 2
## Polymer_ID `n()`
## <chr> <int>
## 1 PA 5
## 2 PC 1
## 3 PET 2
## 4 PLA 1
## 5 POM 1
## 6 PP 3
ggtexttable(plastic_lab_count)
ggtexttable(plastic_lab_filt)
#Its the summarised counts that I need to import into a new df with each sample and
#Some Notes From OpenSpecy Metadata and SOP OpenSpecy uses the same hit indexing as I have used in my Raman pipeline. They do the same baseline removal. My cutoff threshold is slightly lower than their threshold of 0.3, whereas due to degredation I’m using a cutoff (0.22) designated from the 2019 paper I adopted this identification process from. That study used degraded fishing tools as their reference to create the cutoff. OpenSpecy have an R shiny App I could use for identification but I believe since their algorithyms are the same I am going to continue writing my script using the Hyperspec Package and data structure to make downstream analysis a bit easier and allows for greater flexibility with what I can do with the data.
The main issue here is you need to analyze each spectra one at a time. My script allows for analysis for an entire samples hundreds of spectra in a single run of the code.
#State of the script I want to make this script have the ability to take in a single spectra for a sample up to thousands of spectra in a single sample and be able to identify each plastic polymer and enumerate them ##Steps I have done: Spectral Preprossessing (smoothing, scaling, baseline correction) Make into a single function Spectral Identification (correlations, hit index, selecting highest correlation) Make into a single function - return a df with the identification ##What is my script missing? Data frame with polymer IDs and total for each polymer with additional environmental data Create data frame from which I can add to as I collect samples with sample name, depth, size fraction, and all the collected information. I need to add to the reference library - the one from OpenSpecy with thousands of different polymer reference spectra I want to see if I can make the plots with ggplot and a hyperspec object…think I can by extracting the SPC data from the “HS” obj I want to see if I can apply the same smoothing function as openspecy does as I am using their reference database…Its not necessary as it makes almost no difference in the estimated coefficients and the approxfun cleans it up enough, although may make some correlations lower than they otherwise would be.
There are some important peaks being missed by not including the 500 nm wavelegnth range. I will change the range of the smoothing process from 750 to 500 and change the upper range to 1900 - 2000 nm.
#If I can import OpenSpecy library and get the same values as they do I will be happy to move onto the next step.
open_spec_reflib <- read.csv("/Users/christophercarnivale/Desktop/Dissertation_data/Microplastic_antarctica/raman_library.csv")
open_spec_reflib_metadata <- read.csv("/Users/christophercarnivale/Desktop/Dissertation_data/Microplastic_antarctica/raman_metadata.csv")
#Need to merge libraries and add information to the metadata csv file for my use
#First change the df to fit that I'm merging
plastic_ref_lib_for_merge <- select(plastic_ref_lib, WAVE, INTENSITY, poly_lab)
plastic_ref_lib_for_merge['sample_name'] <- 1
for(i in seq_along(poly_labs)){
plastic_ref_lib_for_merge[which(plastic_ref_lib_for_merge$poly_lab == poly_labs[i]),]['sample_name'] <- rep(seq(623, 623+length(poly_labs), by = 1)[i], nrow(plastic_ref_lib_for_merge[which(plastic_ref_lib_for_merge$poly_lab == poly_labs[i]),]))
}
plastic_ref_lib_for_merge <- select(plastic_ref_lib_for_merge, WAVE, INTENSITY, sample_name, poly_lab)
colnames(plastic_ref_lib_for_merge) <- colnames(open_spec_reflib)#This now makes the libraries compatible for merger
#Create a single hyperspec object with all of the reference spectra in the "spc" portion of the data.
#Vector of values on with to index the reference library dataframe. I dont need to create a hyperspec object UNLESS I need to change the values of the reference library.
OS_plastic_id_name <- unique(open_spec_reflib$sample_name)
#I don't think I need to create a hyperspec object I just need to convert the library to a form in which can be used in a cor analysis. Cor() needs the same number of observations for both inputs for the comparison.
#This essentially means I need to convert the raw library spectra with the same smoothing and approx patterns. I should finish applying the smoothing function into the script and then apply it to the library.
#This doesn't work as there are too many spectra with similar spectrum identity names
test_id_name <- unique(open_spec_reflib_metadata$spectrum_identity)
#Create an correlation matrix to fill that are the dimensions of the plastic reference vs the number of spectra collected within a single sample
cor_table_manyplastic <- as.data.frame(matrix(ncol = length(OS_plastic_id_name), nrow = nrow(potp_hyper_corrected@data[["spc"]]), dimnames = list(NULL, OS_plastic_id_name)))
#Calculating correlations and filling the DF with the values
for(row in 1:nrow(potp_hyper_corrected@data[["spc"]])){
for(i in seq_along(OS_plastic_id_name)){
temp_filter <- dplyr::filter(open_spec_reflib, OS_plastic_id_name == OS_plastic_id_name[i])
#This is the step where I subset the reference into the same form as the imported data processed prior
#Now I need to process the original spectra in the same manner that I have
cor_table_manyplastic[row,i] <- round(cor(potp_hyper_corrected@data[["spc"]][row,], temp_filter$INTENSITY, method = "pearson"), digits = 3)
}
}
#head(open_spec_reflib_metadata)
#head(open_spec_reflib)
#head(plastic_ref_lib_for_merge)
#str(open_spec_reflib)
#MERGING libraries here...
Merge_test <- rbind(plastic_ref_lib_for_merge, open_spec_reflib)
#This works
#Now I have a df from which I can parse out and use for plasic identification
#Does this pipeline hold up to with the new spectral data?
#If so, I need to add in additional reference from openspecy, data which I can download
#Making notes of each step to make it more readable for future reference
setwd("/Users/christophercarnivale/Desktop/Dissertation_data/Raman Spectral Data copy/Antarctic_Microplastic_Raman_Spectra/Test_6_28_23/St_3/Less_20/")
#Creates a list of all file names within the desired folder
#Each folder with house an entire samples spectral data
plastic_test_files <- list.files(path = "/Users/christophercarnivale/Desktop/Dissertation_data/Raman Spectral Data copy/Antarctic_Microplastic_Raman_Spectra/Test_6_28_23/St_3/Less_20/", pattern = "*.txt")
#Index values to be looped over
file_numbers <- seq(plastic_test_files)
#Blank matrix table to be inserted into an hyperspec object
plastic_test_spc_table <- matrix()
#Desired range of spectral data for identification
#data range collected is much larger, ranging form ~100 - ~2500 wavelength
freq_index <- seq(500, 1750, by = 0.1)
#Empty dataframe from which to add in each spectra
all_sample_particle_WV <- as.data.frame(freq_index)
#start of a larger raman data import function need to uncomment for it to work
#Raman_large_import <- function(filepath){
#freq_index <- seq(780, 1750, by = 1)
#all_sample_particle_WV <- as.data.frame(freq_index)
#for loop over .txt files and create a df of spectra
#This loops and adds the smoothed version of the spectra within the wavelength ranges desired, which can be changed at any time by changing the "freq_index" object
for(filename in plastic_test_files){
temp_csv <- read.delim(file = filename, header = F, sep = "\t")
colnames(temp_csv) <- c("Wavelength", "Counts")
temp_csv_matrix <- as.matrix(temp_csv)
approx_test_fun <- approxfun(x = temp_csv_matrix[,'Wavelength'], y = temp_csv_matrix[,'Counts'], rule = 2)
new_counts <- approx_test_fun(freq_index)
new_counts_smoother <- sgolayfilt(new_counts, 3,11, m = 1)
all_sample_particle_WV[filename] <- new_counts_smoother
}
#Matrix is in the wrong format and needs to by transposed to fit the hyperspec obj
all_sample_particle_WV_t <- t(all_sample_particle_WV)
#Converting the first row into column names
colnames(all_sample_particle_WV_t) <- all_sample_particle_WV_t[1,]
all_sample_particle_WV_t <- all_sample_particle_WV_t[-1,]
#Extracting the filenames or name of specific spectra being analyzed
potplastic_filename <- rownames(all_sample_particle_WV_t)
#Making hyperspec obj with matrix of spectra with filename meta data
potp_hyper <- as.hyperSpec(all_sample_particle_WV_t, data = data.frame(potplastic_filename))
#This version of as.hyper works and keeps the filenames for each raman spectra
#Baseline correction
potp_baselines <- spc.fit.poly.below(potp_hyper, poly.order = 6)
#Plotting baseline corrections
plot(potp_hyper-potp_baselines)
title(xlab = "Wavelength",ylab = "Arbitrary unit of excitation")
potp_hyper_corrected <- potp_hyper-potp_baselines
#Standard Normal variate normalization
potp_hyper_corrected@data[["spc"]] <- standardNormalVariate(potp_hyper_corrected@data[["spc"]])
#min-max scaling
potp_hyper_corrected@data[["spc"]] <- (potp_hyper_corrected@data[["spc"]] - min(potp_hyper_corrected@data[["spc"]])) / (max(potp_hyper_corrected@data[["spc"]])-min(potp_hyper_corrected@data[["spc"]]))
#}
plot(potp_hyper_corrected, col = rainbow(39),title.args = list(xlab = "Wavelength",ylab = "Arbitrary unit of Intensity"))+legend(500, 1, legend = potplastic_filename,col = rainbow(39))
plot(potp_hyper_corrected)
#Implemented the new smoothing algorithm...now I need to implement this identification with the new library and how that works.
#Extracting unique names from reference library
#poly_labs <- as.character(unique(ref_lib$poly_lab))
#remove the last column (why?)
#poly_labs <- poly_labs[-15]
#This previous version took all samples from the study and not just the reference platic. All of the categorized "." were environmental samples thus necessitating removal. I had a filtered df for plastic reference only so use that df for plastic label names.
poly_labs <-as.character(unique(plastic_ref_lib$poly_lab))
#Create an correlation matrix to fill that are the dimensions of the plastic reference vs the number of spectra collected within a single sample
cor_table_manyplastic <- as.data.frame(matrix(ncol = length(poly_labs), nrow = nrow(potp_hyper_corrected@data[["spc"]]), dimnames = list(NULL, poly_labs)))
#Calculating correlations and filling the DF with the values
for(row in 1:nrow(potp_hyper_corrected@data[["spc"]])){
for(i in seq_along(poly_labs)){
temp_filter <- dplyr::filter(plastic_ref_lib, poly_lab == poly_labs[i])
temp_approx <- approxfun()
cor_table_manyplastic[row,i] <- round(cor(potp_hyper_corrected@data[["spc"]][row,], temp_filter$INTENSITY, method = "pearson"), digits = 3)
}
}
#Creating an empty df to hold the identified plastic info in
plastic_lab <- as.data.frame(matrix(nrow = nrow(cor_table_manyplastic), ncol = 2))
colnames(plastic_lab) <- c("Hit_Index_Value","Polymer_ID")
rownames(plastic_lab) <- plastic_test_files
#Search the correlation matrix for plastic that are the highest correlated to the spectra with the filename (or really particle name).
for (d in 1:nrow(cor_table_manyplastic)){
plastic_lab[d,1]<-cor_table_manyplastic[d,which.max(cor_table_manyplastic[d,])]
plastic_lab[d,2]<-colnames(cor_table_manyplastic[which.max(cor_table_manyplastic[d,])])
}
#Filter spectra that don't meet the spectra limit
plastic_lab_filt <- plastic_lab %>%
rownames_to_column('filename') %>%
dplyr::filter(Hit_Index_Value > 0.22) %>%
column_to_rownames('filename')
#Summarise the counts for each polymer - But I need to have a way to add to a df with polymers unidentified in the sample but we have references for.
plastic_lab_count <- group_by(plastic_lab_filt, Polymer_ID) %>% summarise(n())
plastic_lab_count
ggtexttable(plastic_lab_count)
ggtexttable(plastic_lab_filt)
#Does this pipeline hold up to with the new spectral data?
#If so, I need to add in additional reference from openspecy, data which I can download
#Making notes of each step to make it more readable for future reference
setwd("/Users/christophercarnivale/Desktop/Dissertation_data/Raman Spectral Data copy/Antarctic_Microplastic_Raman_Spectra/7_06_23/Stn_R/Surface/less_20/")
#Creates a list of all file names within the desired folder
#Each folder with house an entire samples spectral data
plastic_test_files <- list.files(path = "/Users/christophercarnivale/Desktop/Dissertation_data/Raman Spectral Data copy/Antarctic_Microplastic_Raman_Spectra/7_06_23/Stn_R/Surface/less_20/", pattern = "*.txt")
#Index values to be looped over
file_numbers <- seq(plastic_test_files)
#Blank matrix table to be inserted into an hyperspec object
plastic_test_spc_table <- matrix()
#Desired range of spectral data for identification
#data range collected is much larger, ranging form ~100 - ~2500 wavelength
#This frequency index is for the inclusion of the oringinal library I can extend the range when I remove the use of the original library by calling the open_spec_ref_lib df instead of the merge test
freq_index <- seq(780, 1750, by = 0.1)
#Empty dataframe from which to add in each spectra
all_sample_particle_WV <- as.data.frame(freq_index)
#start of a larger raman data import function need to uncomment for it to work
#Raman_large_import <- function(filepath){
#freq_index <- seq(780, 1750, by = 1)
#all_sample_particle_WV <- as.data.frame(freq_index)
#for loop over .txt files and create a df of spectra
#This loops and adds the smoothed version of the spectra within the wavelength ranges desired, which can be changed at any time by changing the "freq_index" object
for(filename in plastic_test_files){
temp_csv <- read.delim(file = filename, header = F, sep = "\t")
colnames(temp_csv) <- c("Wavelength", "Counts")
temp_csv_matrix <- as.matrix(temp_csv)
approx_test_fun <- approxfun(x = temp_csv_matrix[,'Wavelength'], y = temp_csv_matrix[,'Counts'], rule = 2)
new_counts <- approx_test_fun(freq_index)
new_counts_smoother <- sgolayfilt(new_counts, 3,11)
all_sample_particle_WV[filename] <- new_counts_smoother
}
#Matrix is in the wrong format and needs to by transposed to fit the hyperspec obj
all_sample_particle_WV_t <- t(all_sample_particle_WV)
#Converting the first column into column names
colnames(all_sample_particle_WV_t) <- all_sample_particle_WV_t[1,]
all_sample_particle_WV_t <- all_sample_particle_WV_t[-1,]
#Extracting the filenames or name of specific spectra being analyzed
potplastic_filename <- rownames(all_sample_particle_WV_t)
#Making hyperspec obj with matrix of spectra with filename meta data
potp_hyper <- as.hyperSpec(all_sample_particle_WV_t, data = data.frame(potplastic_filename))
#This version of as.hyper works and keeps the filenames for each raman spectra
#Baseline correction
potp_baselines <- spc.fit.poly.below(potp_hyper, poly.order = 6)
#Plotting baseline corrections
plot(potp_hyper-potp_baselines)
title(xlab = "Wavelength",ylab = "Arbitrary unit of excitation")
potp_hyper_corrected <- potp_hyper-potp_baselines
#Standard Normal variate normalization
potp_hyper_corrected@data[["spc"]] <- standardNormalVariate(potp_hyper_corrected@data[["spc"]])
#min-max scaling
potp_hyper_corrected@data[["spc"]] <- (potp_hyper_corrected@data[["spc"]] - min(potp_hyper_corrected@data[["spc"]])) / (max(potp_hyper_corrected@data[["spc"]])-min(potp_hyper_corrected@data[["spc"]]))
plot(potp_hyper_corrected, col = rainbow(40),title.args = list(xlab = "Wavelength",ylab = "Arbitrary unit of Intensity"))
plot(potp_hyper_corrected)
#Implemented the new smoothing algorithm...now I need to implement this identification with the new library and how that works.
#Extracting unique names from reference library
#poly_labs <- as.character(unique(ref_lib$poly_lab))
#remove the last column (why?)
#poly_labs <- poly_labs[-15]
#This previous version took all samples from the study and not just the reference platic. All of the categorized "." were environmental samples thus necessitating removal. I had a filtered df for plastic reference only so use that df for plastic label names.
poly_labs <-as.character(unique(plastic_ref_lib$poly_lab))
#Create an correlation matrix to fill that are the dimensions of the plastic reference vs the number of spectra collected within a single sample
cor_table_manyplastic <- as.data.frame(matrix(ncol = length(OS_plastic_id_name), nrow = nrow(potp_hyper_corrected@data[["spc"]]), dimnames = list(NULL, OS_plastic_id_name)))
#Calculating correlations and filling the DF with the values
for(row in 1:nrow(potp_hyper_corrected@data[["spc"]])){
for(i in seq_along(OS_plastic_id_name)){
temp_filter <- dplyr::filter(Merge_test, sample_name == OS_plastic_id_name[i])
approx_test_fun <- approxfun(x = temp_filter[,'wavenumber'], y = temp_filter[,'intensity'], rule = 2)
new_counts <- approx_test_fun(freq_index)
#This frequency index is for the inclusion of the oringinal library I can extend the range when I remove the use of the original library by calling the open_spec_ref_lib df instead of the merge test
new_counts_smoother <- sgolayfilt(new_counts, 3,11)
cor_table_manyplastic[row,i] <- round(cor(potp_hyper_corrected@data[["spc"]][row,], new_counts_smoother, method = "pearson"), digits = 3)
}
}
#Debugging... the previous code works for only a small subset of the library. Most of the library fills with NAs
for(row in 1:nrow(potp_hyper_corrected@data[["spc"]])){
for(i in seq_along(OS_plastic_id_name)){
temp_filter <- dplyr::filter(Merge_test, sample_name == OS_plastic_id_name[i])
print(temp_filter)
approx_test_fun <- approxfun(x = temp_filter[,'wavenumber'], y = temp_filter[,'intensity'], rule = 2)
new_counts <- approx_test_fun(freq_index)
#This frequency index is for the inclusion of the oringinal library I can extend the range when I remove the use of the original library by calling the open_spec_ref_lib df instead of the merge test
new_counts_smoother <- sgolayfilt(new_counts, 3,11)
cor_table_manyplastic[row,i] <- round(cor(potp_hyper_corrected@data[["spc"]][row,], new_counts_smoother, method = "pearson"), digits = 3)
}
}
#Creating an empty df to hold the identified plastic info in
plastic_lab <- as.data.frame(matrix(nrow = nrow(cor_table_manyplastic), ncol = 2))
colnames(plastic_lab) <- c("Hit_Index_Value","Polymer_ID")
rownames(plastic_lab) <- plastic_test_files
#Search the correlation matrix for plastic that are the highest correlated to the spectra with the filename (or really particle name).
for (d in 1:nrow(cor_table_manyplastic)){
plastic_lab[d,1]<-cor_table_manyplastic[d,which.max(cor_table_manyplastic[d,])]
plastic_lab[d,2]<-colnames(cor_table_manyplastic[which.max(cor_table_manyplastic[d,])])
}
#Filter spectra that don't meet the spectra limit
plastic_lab_filt <- plastic_lab %>%
rownames_to_column('filename') %>%
dplyr::filter(Hit_Index_Value > 0.2) %>%
column_to_rownames('filename')
#Summarise the counts for each polymer - But I need to have a way to add to a df with polymers unidentified in the sample but we have references for.
plastic_lab_count <- group_by(plastic_lab_filt, Polymer_ID) %>% summarise(n())
plastic_lab_count
ggtexttable(plastic_lab_count)
ggtexttable(plastic_lab_filt)
write.csv(plastic_lab_filt, file = "/Users/christophercarnivale/Desktop/Dissertation_data/Microplastic_antarctica/all_plastic_correlations_indexMIN.csv")
write.csv(plastic_lab_count, file = "/Users/christophercarnivale/Desktop/Dissertation_data/Microplastic_antarctica/filtered_plastic_correlations_indexMIN.csv")
derivative_ref_lib <- readRDS("derivative.rds")
derivative_ref_lib[["spectra"]][1:5,1:5]
## 00002e4e3fac430aa1fdfea6e26f85e4 00061dd49cbb71549cf3d530b894ba92
## 1: NA NA
## 2: NA 0.07754215
## 3: NA 0.07976792
## 4: NA 0.08517078
## 5: NA 0.09375073
## 00087f78d45c571524fce483ef10752e 0008a60c1af45a76ffb91b9cbe1a32e4
## 1: NA NA
## 2: NA NA
## 3: NA NA
## 4: NA NA
## 5: NA NA
## 000902ae526db452960c5782843081b4
## 1: NA
## 2: NA
## 3: NA
## 4: NA
## 5: NA